423 Firefox Flaws Fixed as Browser Gains Support for Claude, Mythos, and More

Mozilla has successfully identified and patched 423 latent security vulnerabilities in Firefox using advanced artificial intelligence models, notably Claude Mythos Preview.

Two weeks after initially announcing their AI-assisted security initiative, Firefox developers have shared a behind-the-scenes look at how they engineered a highly effective threat-hunting pipeline.

This milestone marks a significant shift in open-source security, demonstrating that AI-generated bug reports have evolved from being inaccurate, unwanted noise into high-fidelity, actionable intelligence that dramatically hardens software against sophisticated attacks.

The breakthrough in automated vulnerability discovery was driven by a combination of vastly improved AI capabilities and Mozilla’s innovative approach to harnessing them.

Developers integrated agentic AI systems directly into their existing fuzzing infrastructure, enabling models such as Claude Opus 4.6 and Claude Mythos Preview to test hypotheses and generate reproducible proof-of-concept exploits dynamically.

Unlike early AI audits that produced high rates of false positives, this new pipeline successfully parallelized bug hunting across multiple virtual machines.

The system autonomously tracked bugs, filtered out unreproducible speculation, and navigated complex multiprocess browser engine code to uncover vulnerabilities that traditional fuzzers missed.

To illustrate the power of this AI-driven approach, Mozilla took the rare step of publicly disclosing a sample of the discovered flaws shortly after issuing security advisories.

The AI models successfully uncovered deeply hidden legacy issues, including a 15-year-old flaw in the HTML legend element, triggered by a precise orchestration of recursion stack depth limits and cycle collection.

Additionally, the system found a 20-year-old XSLT vulnerability where reentrant calls caused a hash table to free its backing store prematurely.

The pipeline also identified a remarkably small testcase that bypassed clamping by appending thousands of rows to HTML tables, overflowing a layout bitfield in a way that remained undetected for years.

Beyond legacy bugs, the AI proved exceptionally proficient at discovering complex sandbox escapes and Use-After-Free (UAF) vulnerabilities. The models engineered exploits that manipulated WebAssembly garbage collection structures to create fake-object primitives.

They also triggered reliable race conditions over Inter-Process Communication (IPC) by manipulating IndexedDB reference counts, and flooded WebTransport with certificate hashes to force memory corruption.

Furthermore, the AI bypassed in-process sandboxing for third-party libraries by exploiting verification gaps and simulated malicious DNS servers to trigger buffer over-reads during HTTPS parsing. These sandbox escapes require intricate reasoning and demonstrate the models’ ability to piece together full-chain compromises.

Despite finding hundreds of bugs, the AI also validated the strength of Firefox’s existing layered defenses. The models repeatedly failed to bypass process sandboxing via prototype pollution because previous architectural changes had frozen prototypes by default.

Ultimately, addressing this unprecedented volume of security flaws required a massive collaborative effort, with over 100 contributors assisting in triaging, reviewing, and shipping patches across recent Firefox updates, including versions 149.0.2, 150.0.1, and 150.0.2.

Looking ahead, Mozilla plans to integrate this AI analysis directly into its continuous integration system to scan new patches as they land in the codebase. By shifting from file-based scanning to patch-based scanning, the development team expects to catch vulnerabilities before they are ever merged into production builds.

Mozilla is now urgently advising other software projects to implement similar AI-driven security harnesses to secure their ecosystems against emerging threats.

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