Partnering with Mozilla to improve Firefox’s security
Anthropic collaborated with Mozilla to use the Claude Opus 4.6 AI model to find software vulnerabilities in Firefox. Over a two-week period, Claude discovered 22 vulnerabilities, 14 of which were classified by Mozilla as high-severity. This effort highlights the ability of AI models to accelerate vulnerability detection in complex software codebases.
The Core Development
AI models can now independently identify high-severity vulnerabilities in complex software. As we recently documented, Claude found more than 500 zero-day vulnerabilities (security flaws that are unknown to the software’s maintainers) in well-tested open-source software.
In this post, we share details of a collaboration with researchers at Mozilla in which Claude Opus 4.6 discovered 22 vulnerabilities over the course of two weeks. Of these, Mozilla assigned 14 as high-severity vulnerabilities —almost a fifth of all high-severity Firefox vulnerabilities that were remediated in 2025. In other words: AI is making it possible to detect severe security vulnerabilities at highly accelerated speeds.
As part of this collaboration, Mozilla fielded a large number of reports from us, helped us understand what types of findings warranted submitting a bug report, and shipped fixes to hundreds of millions of users in Firefox 148.0 . Their partnership , and the technical lessons we learned, provides a model for how AI-enabled security researchers and maintainers can work together to meet this moment.
Strategic Perspective & Ecosystem Impact
In late 2025, we noticed that Opus 4.5 was close to solving all tasks in CyberGym , a benchmark that tests whether LLMs can reproduce known security vulnerabilities. We wanted to construct a harder and more realistic evaluation that contained a higher concentration of technically complex vulnerabilities, like those present in modern web browsers. So we built a dataset of prior Firefox common vulnerabilities and exposures (CVEs) to see if Claude could reproduce those.
We chose Firefox because it’s both a complex codebase and one of the most well-tested and secure open-source projects in the world. This makes it a harder test of AI’s ability to find novel security vulnerabilities than the open-source software we previously used to test our models. Hundreds of millions of users rely on it daily, and browser vulnerabilities are particularly dangerous because users routinely encounter untrusted content and depend on the browser to keep them safe.
Our first step was to use Claude to find previously identified CVEs in older versions of the Firefox codebase. We were surprised that Opus 4.6 could reproduce a high percentage of these historical CVEs, given that each of them took significant human effort to uncover. But it was still unclear how much we should trust this result because it was possible that at least some of those historical CVEs were already in Claude’s training data.
Why it matters
The collaboration demonstrates how AI models can accelerate vulnerability detection in complex codebases like web browsers, while also highlighting the emerging capability and current limitations of LLMs in automated exploit development.