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Mozilla says 271 vulnerabilities found by Mythos and "almost no false positives"

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Why This Matters

Mozilla's use of AI with Mythos to detect 271 security vulnerabilities in Firefox demonstrates significant progress in leveraging machine learning for cybersecurity. This development offers a promising tool for faster, more accurate vulnerability detection, potentially enhancing software security across the industry and providing consumers with safer digital experiences.

Key Takeaways

The disbelief was palpable when Mozilla’s CTO last month declared that AI-assisted vulnerability detection meant “zero-days are numbered” and “defenders finally have a chance to win, decisively.” After all, it looked like part of an all-too-familiar pattern: Cherry-pick a handful of impressive AI-achieved results, leave out any of the fine print that might paint a more nuanced picture, and let the hype train roll on.

Mindful of the skepticism, Mozilla on Thursday provided a behind-the-scenes look into its use of Anthropic Mythos—an AI model for identifying software vulnerabilities—to ferret out 271 Firefox security flaws over two months. In a post, Mozilla engineers said the finally ready-for-prime-time breakthrough they achieved was primarily the result of two things: (1) improvement in the models themselves and (2) Mozilla’s development of a custom “harness” that supported Mythos as it analyzed Firefox source code.

“Almost no false positives”

The engineers said their earlier brushes with AI-assisted vulnerability detection were fraught with “unwanted slop.” Typically, someone would prompt a model to analyze a block of code. The model would then produce plausible-reading bug reports, and often at unprecedented scales. Invariably, however, when human developers further investigated, they’d find a large percentage of the details had been hallucinated. The humans would then need to invest significant work handling the vulnerability reports the old-fashioned way.

Mozilla’s work with Mythos was different, Mozilla Distinguished Engineer Brian Grinstead said in an interview. The biggest differentiating factor was the use of an agent harness, a piece of code that wraps around an LLM to guide it through a series of specific tasks. For such a harness to be useful, it requires significant resources to customize it to the project-specific semantics, tooling, and processes it will be used for.

Grinstead described the harness his team built as “the code that drives the LLM in order to accomplish a goal. It gives the model instructions (e.g., ‘find a bug in this file’), provides it tools (e.g., allowing it to read/write files and evaluate test cases), then runs it in a loop until completion.” The harness gave Mythos access to the same tools and pipeline that human Mozilla developers use, including the special Firefox build they use for testing.