- Anthropic Claude Opus 4.6 discovers 22 security flaws in Firefox
- Mozilla confirmed 14 high severity vulnerabilities patched in Firefox 148
- AI model demonstrated accelerated, human-like vulnerability detection
Anthropic says it found nearly two dozen vulnerabilities in the latest version of Mozilla’s Firefox browser, including some that could have caused serious damage.
In a new blog post, Anthropic said it partnered with researchers at Mozilla and, over the course of a couple of weeks, scanned nearly 6,000 C++ files using Claude Opus 4.6.
Opus 4.6 is the latest version of Anthropic’s most powerful large language model (LLM), which was released in early February 2026 and has been advertised as a must-have tool in every cyber defender’s arsenal, stating that it is “markedly better” at finding high severity vulnerabilities.
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Great success
After analyzing popular open source repositories and finding more than 500 flaws, Anthropic set its sights on Firefox, mainly because it is “complex and one of the most well-tested and secure open source projects in the world.” In other words, he really wanted to prove his point by finding a product that is generally considered great and safe.
The team ran the experiment for two weeks, and in that period, Opus 4.6 managed to find 22 vulnerabilities. Mozilla rated 14 of them as high severity. In total, Anthropic submitted a total of 112 unique reports, most of which were addressed in Firefox 148. The rest were said to be fixed in future versions.
Anthropic frames this as a huge success, saying that Opus 4.6 discovered in two weeks about one-fifth as many high-severity vulnerabilities as Mozilla fixed throughout 2025.
“AI is making it possible to detect serious security vulnerabilities at very accelerated speeds,” they said. Previously, Anthropic said Opus 4.6 stood out for the way it found vulnerabilities “out of the box, without task-specific tools, custom scaffolding, or specialized prompts.”
He also added that unlike fuzzing, which is a standard vulnerability hunting technique, Opus works by reasoning about the code “as a human researcher would,” meaning it searched past fixes to find similar bugs that weren’t fixed, detected patterns that tend to cause problems, and understood the logic “well enough to know exactly what input would break it.”
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