GoKawiilWe pointed our AI audit pipeline at Cloudflare's CIRCL experimental cryptography library and confirmed seven real bugs, from a critical float64 precision loss in threshold RSA to a complete access-control break in attribute-based encryption. All seven are now fixed upstream. This is the first post in a series on bugs our agents found across open source cryptography.
At zkSecurity we are building zkao, an AI audit agent. The goal is simple to state and hard to do: keep an AI looking at your code, continuously, until no bugs remain that other AI tools can find. We wrote about why that approach matters in zkao: Security That Compounds.
Building zkao has been an iterative process, with the end goal of creating an automated auditor capable of finding all bugs that can be detected by AI. This has involved brainstorming new ideas and techniques, systematically encoding the expertise of zkSecurity's security researchers into zkao, ensuring that it detects the latest and most severe vulnerabilities without being biased toward benchmarks, and, importantly, continuously conducting experiments to understand what works, what does not, how models evolve, and to deepen our understanding of bug finding with AI. Some of those experiments produce things worth sharing on their own, independent of the product, and that is what this post series is about.
There is also a second motivation. These experiments are how we build a benchmark suite for zkao, and along the way they keep surfacing insights into how LLMs actually reason about cryptography: where they are sharp, where they are blind, and how to amplify the former and contain the latter. While the bugs are the visible output, the reasoning patterns are the part we care about most.
A few months ago, we started running experiments on selected codebases. We used LLMs to scan a few open source cryptography projects, in two configurations:
LLM only, with a simple prompt. LLM with skills, where the skills were maintained by experts on our team.
Then, for the important projects where the LLMs found real vulnerabilities, we also ran zkao to see whether it could have detected the same issues on its own. In most cases, zkao not only found all of them but also identified more complex and more severe ones.
The results were good enough that we decided to write them up. We are starting this series with Cloudflare's CIRCL, a library of advanced and post-quantum cryptography. On CIRCL, our pipeline produced many candidate findings, and seven of them are worth reporting here. All seven are now fixed upstream. Most of them were confirmed and awarded bounties under Cloudflare's program on HackerOne.
Clarification: the AI produced candidate findings, not final reports. Humans on our team still validated each issue, checked exploitability, minimized the POC where needed, and handled disclosure. That human-in-the-loop step still matters a lot, because AI candidate findings are cheap while trustworthy reports are not. Minimizing that step is one of the main things zkao is built to do, and while it is still a work in progress, the current version already takes on much of this validation work itself.
Severities and fixes at a glance
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