Article URL: https://blog.google/security/chrome-stronger-with-every-update/ Comments URL: https://news.ycombinator.com/item?id=49120097 Points: 314 # Comments: 277

How Chrome is using AI to improve vulnerability discovery, triage, and patching. We’re living through a massive shift in the software security industry. Large Language Models (LLMs) are unlocking unprecedented capabilities for automated vulnerability discovery, scaling far beyond the limits of human security expertise, and requiring new approaches for staying ahead of attackers. This means deploying AI models at scale to find and fix hundreds of security bugs, faster than ever, with the goal of achieving greater resilience and comprehensive remediation. Some software bugs have security implications. While a purely functional bug might result in a frustrating UI freeze, a security bug (or vulnerability) can be used to build an exploit. Exploits allow attackers to perform malicious actions on a victim’s computer, such as reading private data, or controlling their machine without their knowledge. The Chrome Security team has been using LLMs for years. In 2023 we developed ways to use LLMs to increase security fuzzing coverage and performance. In 2024, we worked with Project Zero on Naptime, giving LLMs specialized tools for vulnerability research. And in 2025, we collaborated with DeepMind and Project Zero on Big Sleep, an AI vulnerability discovery agent that successfully found bugs in the V8 JavaScript engine and graphics stack. In early 2026, we built an agent harness that used Gemini to find vulnerabilities across the broader Chrome codebase with higher efficiency and lower false positives. One of the bugs we found was a sandbox escape that would allow a compromised renderer to trick the browser into reading local files — a bug that quietly survived in our codebase for more than 13 years! For many of us, this moment cemented the potential of AI-powered vulnerability detection. We’ve built all of this with safety in mind, and have put in place guardrails to mitigate the risk of AI behaving unexpectedly. Our AI analyzes source code strictly at rest, operating on locked-down machines that lack general internet access. We also utilize a dedicated setup for these internal scans that intercepts all network requests, employing strict allowlists based on the initiating application and destination, blocking any suspicious model activity. Furthermore, we never run models in an unrestricted mode, and we strictly limit our subagents from modifying the local system or accessing files outside of designated source code directories. AI-powered vulnerability detection complements our existing security testing infrastructure. For example, fuzzing continues to be especially effective at finding bugs that arise from long-range interactions between disparate parts of our codebase, or those requiring a combination of seemingly unrelated operations.