Google’s experimental AI framework, Big Sleep (formerly Project Naptime) and a collaborative effort between Google Project Zero and DeepMind has achieved a breakthrough by uncovering a previously unknown, exploitable vulnerability in the widely-used SQLite open-source database. This zero-day memory safety flaw, which results from a stack buffer underflow that could lead to crashes or arbitrary code execution, was found by analyzing recent code commits before it reached an official release, ensuring user safety. Traditional fuzzing, which tests software by feeding random or invalid data to trigger errors, failed to detect the issue, underscoring Big Sleep’s potential as an advanced cybersecurity tool. Big Sleep leverages a large language model (LLM) to simulate human-like reasoning and code comprehension, allowing it to navigate codebases, run sandboxed Python scripts, and debug vulnerabilities with remarkable efficiency. This AI-driven approach specifically aids in identifying vulnerability variants (modifications of previously known flaws) which account for a significant portion of zero-day exploits but often evade traditional tools. Despite the success, Google notes that results are experimental and suggests that targeted fuzzers still play a critical role in vulnerability research. However, this AI breakthrough highlights the potential of LLMs to help defenders stay ahead of attackers by identifying and fixing vulnerabilities before they can be exploited, representing a promising step forward in proactive cybersecurity. CTIX analysts will continue to report on critical vulnerabilities and the efforts taken to defend against their exploitation.