Vulnhalla: Picking the true vulnerabilities from the CodeQL haystack (www.cyberark.com)

Vulnhalla, a new vulnerability detection tool, combines large language model (LLM) reasoning with CodeQL's static analysis to sift through potential security flaws in code. This innovative approach drastically reduces false positives that often overwhelm security teams, enabling them to focus on true vulnerabilities. Within just two days and a minimal budget of under $80, Vulnhalla successfully identified multiple vulnerabilities across significant software like the Linux Kernel and FFmpeg, underscoring the tool's efficiency and effectiveness in real-world applications. The significance of Vulnhalla for the AI/ML community lies in its strategy to tackle the well-known challenges in vulnerability research: the "WHERE" problem (determining which parts of the extensive codebase merit investigation) and the "WHAT" problem (identifying the specific type of bugs to search for). By integrating static analysis with LLM capabilities, Vulnhalla provides a more comprehensive context for assessing alerts generated by CodeQL, significantly enhancing the accuracy of vulnerability detection. This hybrid approach not only promises to accelerate security analysis but also addresses the pressing need for automating vulnerability detection processes amidst growing codebases and increasing software complexity.