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RMM-ber this ransomware.

⁠Ismael Valenzuela⁠, Vice President of Labs, Threat Research and Intelligence at ⁠Arctic Wolf⁠, sits down with Dave to discuss their work tracking Anubis. Arctic Wolf Labs details a series of 2026 Anubis ransomware intrusions, revealing affiliates using stolen VPN credentials and exploiting CitrixBleed 2 to gain initial access. Attackers then blended into legitimate IT activity by deploying RMM tools, using RDP and PsExec for lateral movement, stealing credentials, and establishing tunnels and proxies for persistence and exfiltration. The research highlights a repeatable attack chain defenders can disrupt before encryption, from suspicious remote access and unauthorized RMM deployment to credential theft, security-tool tampering, and ransomware execution.

Who let the AI hack?

Today we are joined by ⁠Crystal Morin⁠, Senior Cybersecurity Strategist, and ⁠Michael Clark⁠, Senior Director of Threat Research, at ⁠Sysdig⁠, sharing their work on "LLMjacking evolved: Attackers are using stolen AI compute to build offensive agentic tools." The Sysdig Threat Research Team observed an attacker abusing an exposed, unauthenticated Ollama server as the “brain” for an automated offensive security tool. The AI-powered framework can fingerprint services, identify vulnerabilities, craft exploits, extract credentials, and orchestrate attacks toward command execution, with researchers capturing the tool while it was still under active development. The activity highlights how LLMjacking is evolving from simply stealing AI compute for profit into using stolen model capacity to build increasingly autonomous offensive capabilities.

A RAT in the spreadsheet.

Today we are joined by ⁠Aaron Beardslee⁠, Manager of Threat Research at ⁠Securonix⁠, discussing "Analyzing SHEET#CREEP: SHEETCREEP is up again with different config obfuscation." Securonix researchers have identified an evolved version of the SHEETCREEP espionage campaign, using a diplomatic-themed ISO phishing lure to deliver a C# remote access trojan targeting Indian diplomatic interests. The malware abuses the Google Sheets API as a stealthy command-and-control channel, with researchers identifying 91 active victim tabs, including a high-confidence target in Pakistan. The campaign, assessed with moderate confidence as linked to Pakistan-aligned APT36, has added XOR-obfuscated configurations and other anti-analysis techniques to evade detection and maintain persistent access.

The botnet that scouts before it strikes.

Today we are joined by ⁠Ian Goldin⁠, Senior Lead Information Security Engineer, and ⁠Mike Horka⁠, Principal Information Security Engineer, from ⁠Lumen's Black Lotus Labs⁠, discussing their research entitled "Expanded JDY IoT and SOHO botnet enables rapid vulnerability exploitation." Black Lotus Labs has uncovered a major resurgence of the JDY botnet, a China-nexus reconnaissance network now comprising more than 1,500 compromised SOHO and IoT devices. The botnet uses these devices to conduct targeted scanning and fingerprinting, helping threat actors rapidly identify vulnerable infrastructure—sometimes within hours of a new vulnerability disclosure—and appears to have a particular focus on U.S. military-related networks. The research highlights how compromised routers and IoT devices can be turned into distributed reconnaissance infrastructure that evades traditional IP-based defenses and supports follow-on exploitation.

A little help from your search engine.

Today we are joined by ⁠Brian Hussey⁠, SVP of Howler Cell Threat Services at ⁠Cyderes⁠, discussing their work on "Bad Ads, Worse Binaries: Fake Claude Code Installer Drops Infostealer." Howler Cell identified an SEO poisoning campaign targeting people searching for Claude Code installation guides, using a fake Anthropic page and a ClickFix lure to trick victims into running a malicious MSHTA command. The attack uses a six-stage, largely fileless chain that employs an MP3/HTA polyglot, PowerShell obfuscation, AMSI bypasses, per-victim infrastructure, and in-memory execution to evade detection. The final payload is a .NET infostealer that steals credentials, while Anthropic and the legitimate Claude Code installation process were not compromised.

Top 10 Malware Threats of the Week – AsyncRAT, Remcos, and Xworm Lead the Surge

Global malware activity climbed sharply over the past week, with remote access trojans (RATs), information stealers, and loaders all posting significant week-over-week gains, according to threat sample uploads tracked by ANY.RUN.

AsyncRAT topped the chart with 211 uploads, edging out Remcos at 196 and Xworm at 183. This trend signals that RAT-based intrusions remain the dominant tactic for cybercriminals seeking persistent, hands-on-keyboard access to compromised Windows systems.

AsyncRAT held the number one position with 211 total uploads and a modest weekly increase of two samples, reflecting its status as one of the most consistently deployed .NET-based remote access trojans in the current threat landscape. The malware is typically delivered through phishing email attacks containing malicious attachments or links.

Once installed, it grants attackers full remote command execution, keylogging, screen capture, and data exfiltration capabilities.

Recent campaigns have shown AsyncRAT operators abusing trusted cloud infrastructure such as Cloudflare’s free-tier services and TryCloudflare tunnels to host payload delivery servers, making detection significantly harder for conventional security tools.

Top 10 Malware Threats of the Week

Remcos RAT recorded the largest single gain among the top three, rising by 59 samples to reach 196 total uploads, underscoring an intensifying wave of espionage and surveillance-driven campaigns.

Originally marketed as a legitimate remote administration tool, Remcos has evolved into a favored espionage and credential-theft platform for both cybercriminals and initial access brokers.

Newer variants observed in early 2026 have shifted toward real-time surveillance, streaming live webcam footage and transmitting keystrokes instantly rather than waiting to exfiltrate stored data, effectively turning infected machines into live monitoring feeds for attackers.

Malware FamilyWeekly Sample UploadsWeekly Volume ChangePrimary Threat Vector
AsyncRAT211+2Remote Access Trojan (.NET)
Remcos RAT196+59Surveillance & Espionage RAT
Xworm183+16Modular Malware-as-a-Service
AgentTesla172+51Keylogger & Info Stealer
Stealc159+67Information Stealer
Vidar157+6Browser & Wallet Stealer
DonutLoader140+16Shellcode / Secondary Loader
Lumma Stealer126+27Credential & Wallet Stealer
Formbook97+21Form Grabber / Info Stealer
Snake93-2Keylogger / Info Stealer

Xworm followed closely with 183 uploads and a gain of 16, continuing its reputation as a highly adaptable, modular RAT sold through malware-as-a-service channels.

Recent Xworm campaigns have leveraged multiple file formats and scripting languages, including PowerShell, VBS, HTA, and Office macro exploits such as CVE-2018-0802, to stage payloads and evade endpoint defenses.

Beyond typical RAT functions like keylogging and webcam access, newer Xworm builds also incorporate destructive capabilities to deploy stealthy infostealer payloads, file encryption, and distributed denial-of-service (DDoS) features.

AgentTesla ranked fourth with 172 uploads and a sharp 51-sample increase, reaffirming its long-standing role as one of the most prolific credential-stealing Trojans in circulation.

Close behind, Stealc posted the single largest weekly jump of the entire list, up 67 samples to reach 159 total uploads, followed by Vidar at 157 with a smaller rise of six.

Both are widely used information stealers designed to harvest browser credentials, cryptocurrency wallet data, and session tokens from infected endpoints.

As detailed in the weekly threat metrics published in the ANY.RUN malware analysis, DonutLoader climbed 16 samples to 140 uploads, reflecting its growing role as a delivery mechanism for secondary payloads, while Lumma stealer rose 27 samples to 126. Formbook rounded out the mid-tier with 97 uploads, up 21 for the week.

Snake was the only family among the top ten to decline, dropping two samples to close the week at 93 total uploads, a modest but notable exception amid an otherwise broad surge across nearly every major malware category.

Security teams are advised to prioritize detection rules for phishing-based delivery chains, monitor for anomalous PowerShell and HTA execution, and flag traffic to known Remcos, AsyncRAT, and Xworm command-and-control infrastructure to blunt the impact of this activity spike.

 Strengthen Your SOC by Accelerating Threat Detection & Rapid Investigations. -> Integrate ANY.RUN With Your SOC Now.

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Why we cannot wait for better post-quantum signature algorithms

RSA and ECC, cryptographic algorithms that we have all relied on for decades, are vulnerable to the attack of sufficiently advanced quantum computers. Such quantum computers do not exist yet, but they seem to be coming sooner than expected. Luckily, the solution is already available: migrate to ML-KEM encryption and ML-DSA signatures, which are designed to be resistant to quantum attack. They were standardized in 2024 by the U.S. National Institute of Standards and Technology (NIST) after an eight-year open international competition.

The migration to post-quantum cryptography is in full swing now. At the time of writing, the majority of traffic handled by Cloudflare is already using ML-KEM encryption, and is thus secured against the threat to data posed by harvest-now-decrypt-later attacks. But encryption is only one part of the equation: to be fully secure against quantum computers capable of breaking classical cryptography, we aim to deploy post-quantum signatures to protect authentication systems from unauthorized access. We are targeting 2029 for Cloudflare to be fully post-quantum secure.

ML-DSA, the best all-around post-quantum signature scheme standardized today, has its downsides: it’s much larger on the wire, and many tricks we were able to perform with RSA and ECC simply cannot be done with ML-DSA. There are better post-quantum signature schemes on the horizon: last month, NIST announced that it is advancing nine post-quantum signature schemes to the third round of the “signatures on-ramp”. And a draft standard for FN-DSA (née Falcon), which was picked from the previous competition, is expected imminently.

We have been very interested in advances in post-quantum signature algorithms, and wrote about the progress in 2021, 2022, 2024, and 2025. In this blog post we’ll treat you to the latest developments in great detail.

But first we have to deal with the elephant in the room: These new signature algorithms will not be ready in time for the PQ transition — not even close, as we will see later on. The problem is arriving too soon for us to wait. ML-DSA is available today, and it will have to do for the first migration. As Eric Rescorla wrote in 2024:

You go to war with the algorithms you have, not the ones you wish you had.

Nonetheless, the search for better post-quantum signature algorithms is crucial for several reasons, and we firmly believe it is still the best use of NIST’s limited resources.

Let’s have a look at the signature algorithms in detail. After that we’ll look at the timeline for their availability, and the reasons why we still need them.

The signature algorithms

In the table below, we compare the candidate signature algorithms that progressed to the third round (marked by 🤔), with classical algorithms vulnerable to quantum attack (marked by ❌), and the post-quantum algorithms that are already standardized ( ✅) or soon will be (📝). Each candidate proposes several variants. We list the most relevant variants to TLS, the protocol used to secure connections on the Internet. To explore all variants, check out Thom Wiggers' signatures zoo.

A few more remarks on this table: Most candidates have multiple variants in every security level. We show the most relevant variants for TLS at the 128-bit security level, the gold standard for security. CPU times are taken from the signatures zoo in June 2026, which collected them from the round two submission documents and later advances. Candidates are allowed to make changes for the third round, which will influence these numbers. Some will improve (both in compute and size), whereas others will regress to counter new attacks. Check out the zoo for the latest numbers. We marked FN-DSA and SQIsign signing with a  ⚠️️, as both are hard to implement in a fast and timing side-channel secure manner. LMS signing has a ⚠️, as secure LMS signing requires keeping state across signatures, and the listed signing time assumes a 32MB cache. The 128-24 variant of SLH-DSA is marked with a ⚠️️ as it’s meant to create fewer than 2^24 signatures.

No "all-star" algorithm

One thing that stands out immediately is that the quantum-vulnerable elliptic curves signature algorithm Ed25519 is by far the best all-around choice (ignoring its quantum vulnerability): it has the best numbers in almost every single metric, including public key size, signature size, and signing time. It’s only beaten on verification time, but it’s more than fast enough for the vast majority of applications.

This is quite different than the roster of post-quantum algorithms. Instead of a single "all-star" algorithm, we have roughly two categories of schemes: the "specialists" that approach our trusty elliptic curve signatures on some metrics, but are problematic on others, which make them great in the right deployment scenario. Then there are the “generalists”, such as ML-DSA, which don’t perform as well as elliptic curves on all metrics, but so far as downsides go, are pretty balanced.

Specialists

Let’s start with the specialists.

SQIsign: small signatures / slow signing

If you just look at the bytes on the wire, then SQIsign looks like an almost perfect drop-in replacement for elliptic curve cryptography. With signatures of 148 bytes and public keys of 65 bytes, it beats RSA-2048. Unfortunately there is no free lunch: SQIsign has three weak points. First, it’s the most complex algorithm on the docket. Secondly, its signature creation and verification is quite slow. Finally, it’s difficult to implement signature creation in a timing side-channel secure way and doing so comes with a performance penalty to boot.

That doesn’t sound great so far, but it was much worse: when we had a look back in 2024, there was not yet any timing side-channel secure implementation and signature verification was 20x slower. Furthermore there has been welcome progress on simplifying the scheme.

Despite these dramatic improvements, it is unlikely (side-channel secure) signing will be fast enough in the foreseeable future to be used in typical online cases such as the TLS handshake. However, for offline cases, such as CA signatures or DNSSEC, where it’s the verification time that’s more important than the signing time, SQIsign might have an application.

But the topic we should really discuss is security. SQIsign is based on isogenies. Rather famously, SIKE, another algorithm based on isogenies, got broken badly in a late stage of the first NIST PQC competition that standardized ML-DSA. SIKE is often brought up as a cautionary example showing that post-quantum cryptography could break suddenly. This requires some nuance. First, there were already concerns about SIKE’s security, and in particular the torsion points that led to the break. Because of these concerns, SIKE was not selected for standardization, but deferred to an additional round of evaluation before it was broken. (Indeed, this is an example of the NIST process working well.) SQIsign doesn’t use torsion points, and there is no similar concern as there was for SIKE.

One other notable security property is that the best known attacks on SQIsign are generic brute force, just like with classical attacks on well-selected elliptic curves. This is quite different from RSA, lattices, and multivariate where the attack algorithms have been slowly improving, pushing the parameters towards bigger signatures. Nonetheless, the mathematics behind isogenies is very rich, and compared to the other  algorithms, there is a lot of mathematical attack surface. Still, its security seems more sound than the structured multivariate algorithms we’ll discuss later.

SQIsign is an algorithm with tremendous potential. It’d be a shame to standardize it too early. To the authors, we’d like to share the following wishlist:

  • Ideally verification time is decreased even further, even if this trades off against signing time and signature size: SQIsign signatures are already small enough, and offline signing time has some slack anyway.
  • The timing side-channel secure implementation should be the default, especially if signing time is decreased further, which would tempt some online signing applications.
  • But above all, our wish is for SQIsign to be simplified.

UOV: tiny signatures / huge public keys

UOV (unbalanced oil and vinegar) is a classic multivariate signature algorithm originally proposed in 1999. It has tiny signatures: only 96 bytes. The trade-off? A huge public key: 66kB. That wouldn’t help for a TLS server certificate, whose public key is transmitted over the wire when setting up a connection, but it would be a help for cases where the public key is predistributed.

Let’s take the WebPKI as an example. A typical browser trusts about a hundred root certificates and 30 certificate transparency logs, whose public keys would add up to about 8MB when using UOV.

Since the root certificate is transmitted to clients out of band, one idea is to use a UOV signature there. But this is not a slam dunk; because of its size, a UOV root certificate would be impractical to be cross-signed where the root is used as an intermediate. At the same time, cross signs and intermediates become less attractive anyway with any larger post-quantum signatures. This encourages more root certificates to be included directly with clients. This would again favor UOV, but to a point: if the number of root certificates grows above a thousand, we’d be dealing with more than 66MB of key material, which would make up a substantial portion of the browsers’ download size (e.g., 90MB for Firefox 151.)

Multivariate security

What about the security? Over the years, many variants of UOV have been proposed that use some extra mathematical structure to reduce the size of the public key. These structured multivariate schemes have had a spotty track record with schemes such as Rainbow and GeMMS being broken quite badly. It is important to distinguish these from UOV itself, whose security track record is much better, but not perfect.

As with many cryptographic schemes, there were growing pains in the early years, as basic attacks and parametrization pitfalls were discovered. In fact, the “U” in UOV is a remnant of that: it stands for unbalanced, which is a fix to a parameter-setting mistake in the 1997 oil-and-vinegar scheme on which UOV is based: the original scheme had an equal number of oil and vinegar variables in the quadratic system of equations used as the public key, which turns out to allow for an attack. In case you’re curious about the colorful name: the system of equations contains vinegar x vinegar and oil x vinegar, but no oil x oil terms. It’s like vinaigrette with small separate oil droplets. Back to the history: from 2005 to 2020 was a quiet period for multivariate signatures: understanding of UOV grew, but there weren’t any new attacks on typical parameters.

This changed in 2020 with the discovery of the intersection attack which built on the ideas of the original attack on balanced oil-and-vinegar. The intersection attack removes about 30 bits of security from a then-proposed 128-bit parameter set. A considerable blow, but not fatal: slightly adjusting parameters mitigates the attack completely, with minor increase in key and signature size.

A bigger shock was the 2025 publication of the idea to use wedges to attack multivariate schemes. The initial impact on UOV was minor: only a few bits (again at the 128-bit security level.) The worry was that this idea came out of left field, and it wasn’t clear how far the approach could be taken. That concern was partly justified: the wedges idea was very fruitful and several subsequent attacks have been built on it, reducing security by about 15 bits. However, it also became clear that the wedges attack and generalizations can be seen as a special cases of existing attacks such as the intersection attack over truncated rings — thus much more familiar than we thought. Again, these attacks can be mitigated with only minor increases in key and signature size.

What to make of all of this? Such a history of attacks is not uncommon: over the last 25 years lattices have seen larger reductions in security, although this has calmed down over the recent years. Notwithstanding, lattice-based cryptography deployed in production today uses conservative parameter sets well above 128-bits to hedge against future cryptanalysis. We’d want to do the same with UOV. Signature size only grows linearly with the security level, costing just 260 bytes even at the 256-bit security level. Unfortunately, the public key size is cubic in security level: 446kB for 256-bit. Conveniently, UOV (as most multivariate schemes) has a lot of flexibility in picking parameter sets at various intermediate security levels.

UOV is a foundational scheme with narrow but real use cases. Going forward, we’d like to see a parameter set with a bit of margin above 128 bits, say 160 bits, to hedge against future cryptanalytic improvements.

QR-UOV: small signatures / large public keys

Like SNOVA and MAYO which we’ll discuss later on, QR-UOV is a structured multivariate scheme: it’s a variant of UOV that adds more structure to the public key to reduce its size. The gains are modest: at best we’re looking at 12kB public keys, but signature verification is impractically slow for that particular parameter set. The more realistic parameter sets start at 24kB public keys.

With respect to security, QR-UOV is the only multivariate scheme that did not have to adjust its original (round one) parameters in response to new attacks. This is somewhat surprising as any attack on UOV can also be applied to QR-UOV. The explanation is that the attacks do apply, but the natural parameters for QR-UOV happen to make them ineffective. On the other hand, there were already several attacks known that use the specific extra structure that QR-UOV adds: indeed, for some of the parameter sets, the structure-specific attacks are the best attacks. This should be contrasted with MAYO, where there is no known attack against the extra structure MAYO adds. (We’ll get back to MAYO and SNOVA later in this post.)

Compared to last round, QR-UOV signing and verification time improved significantly, but it is still comparatively slow. All in all, QR-UOV is a hard sell: it adds exploitable structure to UOV without pushing key sizes down to general-purpose sizes.

Hash-based signatures

Stateful hash-based signatures

The very first standardized post-quantum signature algorithms are the stateful hash-based LMS, HSS and XMSS(MT). They have very small public keys, and for many parameter sets the signatures are much smaller than those of ML-DSA-44. To boot, their security is based on that of hashes, which are well-understood and already a cornerstone of cryptography. That makes hash-based signature algorithms a very conservative choice, and there is no need to hedge with higher security levels.

So, what’s the catch?

There are two. The big one is keeping the eponymous state. These stateful hash-based signature schemes are built out of one-time-signature keys which are collected into Merkle trees. The signer has to keep track of which one-time-signature keys have been used, which can be as simple as just a counter. If the signer mucks it up, though, and accidentally uses the same one-time-signature key twice on a different message, then anyone can likely use those two signatures to create their own signature on any message. You have to keep a lot in mind to keep the state correctly. Some considerations: you want to make sure that updates are written to storage before handing out the signature; you don’t want the old state to be restored from a backup; and you can’t export/import a private key from one place to another without agreeing on how to split or keep the state. The state is, as Adam Langley pointed out several years ago, a huge foot-cannon.

Another downside is that the most competitive parameter sets can only create a modest number of signatures. The 1,112 byte signatures (as listed in the table above) can only be used to create about a million signatures. You can explore the trade-offs with this calculator.

Together this leaves a very small niche for stateful hash-based signatures: signers have to be able to keep state; signature size has to be a real concern; and signers have to be OK with a hard limit on the number of signatures.

SLH-DSA: conservative security / large and slow

SLH-DSA is a hash-based signature that doesn’t have the low signature limit and avoids the problem of keeping the state. The basic idea is to make the number of one-time-signature keys so large that you can pick one at random without having to worry about using the same one twice, since the chance of picking the same one twice is diminishingly small. SLH-DSA is a bit more efficient than that, by replacing the one-time-signature key as a building block with a few-time-signature key, where security degrades gracefully if keys are occasionally reused. It still comes at a cost. SLH-DSA has two variants, one that optimizes for small signature size, and one that optimizes for fast signing. The size-optimized one is not small at all at 8kB, and the signing-optimized one is even slower than SQIsign.

Fewer signature variants of SLH-DSA

NIST has proposed to standardize an additional parameter set for SLH-DSA with much smaller signatures, but that can only be used to create about 16 million signatures before security reduces. At 3.8kB the signatures are still larger than those of ML-DSA-44, but the combined public key and signature size is very close. The parameter set was chosen to make signature verification fast at the cost of signing time. The signing time is very bad indeed.

Use cases

So why ever use SLH-DSA? The selling point is the conservative security. For a long-term trusted key that is hard to replace, it could make sense if the application can stomach the large signature and slow verification of the standardized variants or the slow signing time of the newly proposed one. There are two more caveats to add. First, it’s better to set things up so that key algorithms are not burned-in and can be replaced after the fact. And secondly, in most cases systems (such as secure connections with TLS) do not just depend on signatures, but also on key agreement. There is no hash-based key agreement mechanism, so we end up needing to trust something less conservative, like lattices, anyway.

FN-DSA: small key and signatures / subtle signing

Comparing the numbers, FN-DSA-512 (née Falcon) looks much better than ML-DSA-44 on almost every metric: faster verification, smaller public key, and much smaller signatures at 666 bytes. Signing is three times slower, but it’s still 25x faster than RSA-2048. To boot it’s already picked to become FIPS 206. So why don’t we consider FN-DSA to be a general-purpose algorithm?

It’s because it’s difficult to implement FN-DSA signing securely. The most well-known sharp edge of FN-DSA is that it is most naturally and efficiently implemented using hardware-accelerated floating-point arithmetic. This is a first for a cryptographic standard. One big challenge with it is that we have little experience implementing fast floating-point arithmetic in a side-channel safe way. What we know so far is that it’s subtle and not very robust: a safe implementation of FN-DSA signing using the Floating-Point Unit (FPU) for one processor might not be safe for another. Instead of relying on the FPU, the floating point operations can be emulated. This is easier to get right, but about 20 times slower, making it about as slow as RSA-2048. There has been some welcome progress recently to implement FN-DSA signing safely using fixed-point arithmetic, which is much faster than the floating-point emulation. So just use that and FN-DSA is good to go? This presumes a level of awareness that might not be warranted. Anecdotally at conferences, every time we saw a presenter compare post-quantum signature algorithms including FN-DSA in benchmarks, they couldn’t answer whether floating-point emulation was used.

Another consequence of using floating points is that it’s difficult to make test vectors for signing. Just one example of this is that the outcome of a+(b+c) and (a+b)+c are only guaranteed to be close, but not the same. That means that to have useful test vectors, the FN-DSA specification would need to be very precise on the order of floating-point operations. Another example is a*b+c, which can be computed in two steps (multiply and then add), or at once using fused-multiply-add (FMA). The latter is faster, but again gives a slightly different answer as rounding happens only once. Not all processors support FMA, but for those that do, compilers typically automatically use FMA for the performance boost. There are also mathematical optimizations that cause trouble. For instance, the reference implementation computes a value (norm) in a faster roundabout way using Parseval’s theorem. Mathematically the answer is exactly the same, but as floating-points are only an approximation, the resulting value is ever so slightly different. Similarly, the safe fixed-point arithmetic implementation gives slightly different results.

Why is this a problem? It is because it is still the humble test vector that catches most implementation bugs in practice. Other more refined methods like formal verification will certainly catch more, but test vectors are hard to beat in simplicity.

Another surprising sharp edge from not having a fixed implementation is the following. From two deterministic signatures created by slightly different implementations from the same private key, one can derive parts of that private key. FN-DSA does not use deterministic signatures, instead adding a randomizer to thwart this. There is a tension with testing: you need a deterministic interface to test signing, but you don’t want that to be used to create actual signatures.

How to deal with the wiggle room in the FN-DSA specification will undoubtedly be a point of discussion. The discrepancy between the implementations might actually have a silver lining: NIST could decide to generate the test vectors (CAVP) from the fixed-point arithmetic implementation. That the more risky floating-point implementation wouldn’t pass the test vectors would be a feature, not a bug, as it would steer implementations towards the safer fixed-point version!

You can read about a few other interesting sharp edges in this blog post. Stepping back from the specifics, the main point is that FN-DSA is a complicated scheme. It’s not a surprise that it took NIST a couple of years (not counting the current limbo) just to write the draft standard. It’ll take longer than usual for the final standard to come out and for cryptographic libraries to add support. FN-DSA is farther away than it seems. We’ll compare timelines later in this blog post.

If the numbers are still very tempting, there is one last thing you should be aware of: FN-DSA-512 is parametrized for 128-bit security compared to ML-DSA-44’s generous 160 bits. If lattice cryptanalysis improves, there is no middle security level: the next step-up is all the way to FN-DSA-1024 at 256 bits. FN-DSA-1024 has double the key and signature sizes and signing and verifying times of FN-DSA-512. An FN-DSA-1024 signature is still half the size of that of ML-DSA-44, but the public key+signature only differs about 20%.

To close the discussion of FN-DSA, it is good to emphasize that all difficulties with FN-DSA are on the signing side: the verification of an FN-DSA signature is very straightforward. 

General-purpose algorithms

Now let’s turn to the algorithms that are meant to be general-purpose replacements for ML-DSA.

HAWK

HAWK is a curious case. In many aspects it’s similar to FN-DSA: a structured lattice hash-then-sign scheme with similar sizes for signatures and public keys with a missing middle security level. The main benefit of HAWK over FN-DSA is that signing is very fast and doesn’t use floating-point arithmetic, although it’s not a simple algorithm either. This comes with a trade-off: HAWK is based on and introduces a new security assumption, the lattice isomorphism problem (LIP). In 2024, two years after the introduction of HAWK, it was discovered that this problem is easy to solve in the special case of totally real number fields, which aren’t used in HAWK or any other cryptography. In 2025, this attack was extended to a broader class of number fields. This hasn’t yet applied to HAWK, but it’s getting closer. A new paper published in June 2026 suggests there is a way to extend the attack to HAWK. An error has been found in the paper, although it’s yet unclear how fundamental it is to the approach. Regardless, the trajectory is concerning.

Even ignoring the potential attacks, HAWK faces some headwinds: its additional security assumption prevents it from displacing FN-DSA, but its practical benefits (especially considering the lack of middle security level) fall short of that of the structured multivariate candidates. It also doesn't increase diversity in security assumptions, an outcome that NIST is hoping for.

Proof-of-knowledge schemes

FAEST, MQOM, and SDitH all share a similar overall structure. Their public keys are instances of some hard problem and their secret keys are the solutions.

  • A FAEST public key is the AES-encryption of a known plaintext under a secret key.
  • MQOM gets its name from the Multivariate Quadratic problem, which is closely related to (but more conservative than) the cryptographic assumptions underlying the multivariate schemes. The public key is a system of quadratic equations, and the secret key is a solution to that system of equations.
  • SDitH is based on the hardness of the Syndrome Decoding problem for random linear codes. This problem is related to the code-based schemes submitted to the original NIST competition, but these were eliminated in the third round.

In all cases, a signature is a zero-knowledge proof that the signer knows the solution of that hard problem, while at the same time (almost as an afterthought) acknowledging the message-to-be-signed as part of the proof.

Many signature schemes are zero-knowledge proofs like this behind the scenes, notably ML-DSA, SQIsign, and Ed25519. Why don’t we group those with proof of knowledge schemes too?

The difference is generalizability: the zero-knowledge proof used for ML-DSA is only able to prove something about a specific LWE problem as used in ML-DSA: the proof uses mathematical structure in the key. There are ways to create zero-knowledge proofs using lattices for any general statement, but those proof systems are very different from ML-DSA, and would create rather larger signatures on the order of 50kB.

In contrast, the proof system used in FAEST, MQOM, and SDitH can be used to prove arbitrary statements. For instance, FAEST can be modified to use the hard problem of MQOM instead. This leads to a more efficient scheme called KuMQuat. (We’ll get to some performance numbers later on.) Conversely, MQOM can be adjusted to use AES as the hard problem.

This flexibility is great for two reasons. First, it doesn’t require any specific mathematical structure in the hard problem used, and thus we can pick a very conservative problem such as breaking AES. Some problems lead to a more efficient signature than others, as we see with MQ as used in MQOM. MQ is still quite a conservative assumption: it does not contain the hidden subspace used in UOV and thus the other multivariate signatures. Neither the intersection nor wedges attacks apply to it. In fact, the MQ-problem is NP-hard. To be secure, one still needs to pick the correct size of the problem, and although MQ has been studied for quite a while, it certainly has not seen the same scrutiny as deployed algorithms like AES.

The second and greater benefit is that we’re able to create much more than just a plain signature scheme from a general zero-knowledge proof system: we can create blind signatures and even full-fledged anonymous credentials.

Here it’s good to note a limitation: the size of the proofs for all three grows linearly with the statement proven. In technical terms: they’re not succinct like STARKs and LaBRADOR, which outperform them handily for large statements. It’s another example where sometimes it’s better to pick the approach that’s not optimal asymptotically.

Back to advantages: apart from the hard problem chosen, and the security of hash functions, these three schemes don’t require any further security assumption. This makes FAEST as conservative as SLH-DSA.

So what’s the difference except for the chosen hard problem? These schemes started off quite differently, but have been improving and converging since the first round. The proof system in MQOM is a bit simpler than FAEST, but it also does not perform as well: KuMQuat (FAEST+MQ) outperforms MQOM.

Talking about performance, let’s start with a comparison to SLH-DSA. All three schemes have variants that outperform any standardised SLH-DSA parameter set and often by a good margin. SLH-DSA does have one distinct advantage: the verification routine is simpler to implement.

Against ML-DSA-44 the comparison is more interesting. All schemes have a smooth trade-off between runtime and signature size. To illustrate, here are trade-offs reported for KuMQuat (FAEST+MQ.) Verification times are close to signing times.

KuMQuat can be parametrized to have somewhat smaller signatures than ML-DSA-44 at the cost of long signing (and verification) runtime. At the other end, it can have similar signing time as ML-DSA-44, at the cost of larger signatures, although the public key+signature size is still similar.

These schemes have improved quite a bit over the years, and we expect some improvements still. Although they won’t improve upon ML-DSA as dramatically as some of the other schemes considered, their conservative security and especially their potential for broader applications like anonymous credentials make them very appealing. To showcase the flexibility of the underlying zero-knowledge proof system, we’d like each scheme in this category to present numbers on how well they’d perform with a different underlying hard problem.

Structured multivariate: MAYO versus SNOVA

Like QR-UOV discussed earlier, MAYO and SNOVA are variants of UOV that add extra structure to the public key to reduce its size. MAYO and SNOVA take two different approaches: SNOVA makes aggressive bets to get the best performance, whereas MAYO treads carefully with a conservative design.

SNOVA does have impressive performance. Its main parameter set has 248 byte signatures (smaller than RSA-2048!) with only a 1kB public key. It beats every other post-quantum scheme (except SQIsign) on public key+signature size and has great runtime.

MAYO’s performance is nothing to scoff at either. MAYOone has the best verification time and its 454 byte signatures are still smaller than those of FN-DSA-512, HAWK-512, and RSA-4096. Combined with its 1,420 byte public key, MAYOone does slightly fall behind FN-DSA-512 and HAWK-512. However, MAYO takes the lead again if we ask for some security margin. FN-DSA and HAWK have a missing middle security level and thus need to bump all the way to the 256-bit security, whereas MAYO’s granularity can add extra security at the cost of slightly increasing public key and signature sizes.

If that wasn’t good enough, both MAYO and SNOVA allow for a trade-off between signature and public key size. Thus, we can get even smaller signatures for public keys that are transmitted ahead of time. Pushed to the extreme, MAYO becomes UOV.

So far we have discussed performance. What about the security? MAYO adds a “whipping” structure on top of UOV: any attack on UOV will also work for MAYO, but there might be attacks specific to the whipping structure of MAYO. So far no attacks on the whipping structure, and thus on MAYO specifically, have been found. The worst that has happened is that some UOV attacks have affected some MAYO variants more than typical UOV parameter sets, due to the UOV parameter choices that are natural for MAYO.

This is in stark contrast to SNOVA. SNOVA has been hit quite hard on its specific structure several times. In response, the SNOVA team has not just tweaked parameters, but continuously changed the actual structure. Every time, they take the leap forward and propose a new SNOVA with even better performance. We noted this last year and the pattern has continued, whereas MAYO’s basic design is stable.

Furthermore, the structure SNOVA uses can be seen as a special form of the whipping map that MAYO uses. That means that any MAYO-specific attack would apply to SNOVA, but not the other way around.

All in all, we’ve seen a lot of progress on the understanding of multivariate security. NIST wrote that they expect an extra round before standardizing a multivariate scheme. That seems prudent. To us, it’s unclear whether SNOVA would be ready by then, but MAYO so far seems to have matured well.

Timelines

Now, let’s have a look ahead and sketch when these new signature algorithms might become usable.

Progress for ML-DSA so far

It’s illustrative to look at ML-DSA.

After NIST selected Dilithium to become ML-DSA, it took a year to draft a proposal for the standard, and another year for the algorithm standard to be published. The algorithm standard is not enough: protocols need to agree on how to integrate ML-DSA. For certificates that took another year. That’s not the end of it: software needs to add support for ML-DSA and its integration into protocols.

These steps are not purely sequential: work on software implementation of ML-DSA started before the final standard. Also, protocol integration standards are often “done” before they’re a final standard. For instance, the use of ML-DSA in TLS is done, but at the time of writing it’ll take a couple of months before the RFC for that is out. Notably OpenSSL jumped the gun and added support for ML-DSA before the IANA codepoints were assigned. Notably missing still is agreement on which hybrid signatures should be used in TLS (or at all), for which (at the time of writing) no IANA code points have been assigned.

When will these new signature algorithms be ready for use?

So where does that leave us for new signature algorithms? If the FN-DSA draft is released today, and it progresses at the same rate as ML-DSA, then we’d perhaps have some early software support in early 2029, but no significant deployment. Looking at the time it took to write the FN-DSA draft standard, it is likely that the final standard, protocol integrations, and software support will progress slowly as well. We would not expect FN-DSA to be widely available before 2033.

The progress in cryptanalysis of multivariate schemes gave NIST pause: they wrote that they expect multivariate to at least take another round of about two years. On the other hand, multivariate schemes are reasonably easy to implement. That means we might see a multivariate NIST standard in 2031, and wider product availability not earlier than 2034.

NIST is more confident in the security of SQIsign than that of multivariate. Not unlike FN-DSA, SQIsign is a difficult scheme to standardise and implement. At the same time, a lot of progress is made in simplifying SQIsign. It seems likely that SQIsign will make large changes for the third round, and will thus require a fourth round of evaluation. In either case, wide availability before 2035 seems unlikely.

As discussed above, HAWK occupies an awkward middle ground between FN-DSA and structured multivariate candidates. If it were standardized, which seems unlikely even before the recent progress in cryptanalysis, we wouldn’t expect product availability before 2034.

That leaves the proof of knowledge algorithms MQOM, SDitH, and FAEST. We’ve seen dramatic improvements to these schemes over the rounds. If that rate of change holds, it’ll require another round, but if it’s stable now, a proof of knowledge algorithm will be the first new NIST standard to see the light in 2030. If it’s out this early, it’ll likely not outperform ML-DSA dramatically. Nonetheless, it’ll still be very welcome to build anonymous credentials and other primitives beyond signatures.

So, should you wait on one of these signatures for your post-quantum migration? Given recent advances in quantum hardware and software, we don’t believe we can afford to wait. At Cloudflare, we’re aiming to be fully migrated by 2029. None of these signatures will be out in time. Deadlines of most regulators vary between 2030 and 2035. These did not account for recent progress, and we expect them to be adjusted. We saw just this with the June 2026 US executive order setting a 2031 deadline. Even if deadlines weren’t changed, we wouldn’t advise waiting.

Why? Deploying post-quantum signatures in 2034 to beat a 2035 deadline is not enough. In a system of any reasonable size, you can’t upgrade everything all at once. You’ll need a transition period where both post-quantum and traditional signatures are supported. And supporting both allows for a downgrade attack. The most straightforward way to prevent such downgrades is to disable classical cryptography. That will take time, and is frankly not even an option in many sufficiently distributed systems like the WebPKI. We will cover how to deal with downgrades in a future blog post. In the meantime here is some reading if you’re curious. In any case, dealing with downgrades will take time.

It seems clear these new post-quantum signature algorithms will not be ready to use in time for the first migration. So why bother?

Why we still need them

We’ve had 50 years to weave public key cryptography all through our digital society. We have a few short years left to make it all quantum secure. For most of these upgrades the procedure is clear: drop in post-quantum cryptography. Easier said than done: it’s a monumental task. But then there are cases that are fundamentally harder. There's no all-star signature in a post-quantum world, and there are cases where the size of ML-DSA is a problem. With enough resources and stakeholder agreement, systems can be re-engineered to work well with these larger signatures. Indeed, thanks to ongoing re-engineering, the post-quantum WebPKI is shaping up to perform better than the quantum-vulnerable one of today. It is unrealistic to expect that this will happen for every system before it’s too late. Some will have to accept a performance cost. Others will need to deal with the security gap in other ways, such as restricting access, tunneling, more monitoring, or a myriad of other measures that are costly on their own. Once smaller post-quantum signatures arrive, these compensating controls can be removed, and full efficiency and security restored.

An indirect, but no less important benefit of the ongoing NIST competition is its help in furthering post-quantum cryptography beyond basic primitives: it isn’t just key agreement and signatures that are quantum vulnerable. There is a long tail of fancy cryptographic primitives out there used in production, such as anonymous credentials, PAKEs, and threshold signatures to name a few. For most, post-quantum variants are not readily available or are understudied. For some, the same goal can be achieved without fancy cryptography, but with a regrettable regression in subtle privacy goals. NIST cannot run a competition to define a post-quantum standard for each of these specific primitives, but luckily the signatures competition has been a huge help here.

The most clear example is FAEST. Although designed as a signature scheme, its underlying machinery (VOLEitH) can be repurposed in combination with a multivariate scheme like MAYO, to create an efficient post-quantum anonymous credential. Without the signatures competition, VOLEitH wouldn’t be as developed and vetted as it is today.

Many of the candidate schemes briefly point out their usefulness apart from signatures. We hope to see more of the indirect applications of these schemes highlighted.

Despite great signatures and more advanced cryptography on the horizon, we should not forget the task at hand: staying secure in the immediate future.

Build your own vulnerability harness

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.

Two objections, up front

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.

It all starts with a skill

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.

Codifying the skill into a pipeline

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. 

A two-stage vulnerability research workflow

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.

Stage 1: Vulnerability Discovery Harness (VDH)

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.

Dynamic threat modeling

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.

Micro-forks and the wishlist

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."

Fleet-wide cross-repo tracing

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.

Making findings you can trust

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.

Stage 2: Vulnerability Validation System (VVS)

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)

Deduping

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.

Contextual judgment

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.

Automated fixing

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. 

What it costs

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.

How we tell it's working

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.

Our “North Star” metrics: measuring real-world velocity

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.

How we’re handling all of this patching

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.

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