Vulnerability detection: From zero-days to autonomous pentesting
LLMs’ semantic code understanding and contextual reasoning supply a major benefit over conventional, signature-based static analyzers, particularly within the discovery of unknown threats earlier than malicious actors discover and exploit them.
LLMs have proven extraordinary potential in figuring out unknown, unpatched flaws (zero-days). These fashions considerably outperform typical static analyzers, notably in uncovering delicate logic flaws and buffer overflows in novel software program. As an example, Google’s Massive Sleep mission used an LLM to establish a zero-day vulnerability within the essential SQLite database used throughout the business.
One other instance is XBOW, which is an autonomous AI penetration testing agent that leverages LLMs to simulate real-world assaults the identical method 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 skilled human hackers find a broad vary of vulnerabilities (e.g., injection flaws, XSS).
