Vulnerability detection: From zero-days to autonomous pentesting
LLMs’ semantic code understanding and contextual reasoning offer a significant advantage over traditional, signature-based static analyzers, especially in the discovery of unknown threats before malicious actors find and exploit them.
LLMs have shown extraordinary potential in identifying unknown, unpatched flaws (zero-days). These models significantly outperform conventional static analyzers, particularly in uncovering subtle logic flaws and buffer overflows in novel software. For instance, Google’s Big Sleep project used an LLM to identify a zero-day vulnerability in the critical SQLite database used across the industry.
Another example is XBOW, which is an autonomous AI penetration testing agent that leverages LLMs to simulate real-world attacks the same way a human counterpart would do. XBOW achieved the #1 spot on the HackerOne US Leaderboard, demonstrating that AI can match and, in some benchmarks, surpass expert human hackers in finding a broad range of vulnerabilities (e.g., injection flaws, XSS).
