This research explores integrating Large Language Models into static analysis tools to reduce false positives in vulnerability detection, particularly in complex codebases.

• Addresses the precision-scalability trade-off that plagues traditional static analysis
• Leverages LLMs' code understanding capabilities to improve vulnerability identification
• Focuses on practical application for large-scale systems like the Linux kernel
• Proposes a hybrid approach combining traditional static analysis with AI-powered insights

For engineering teams, this represents a significant advancement in automated security tooling that could dramatically improve code quality while reducing manual review effort.

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs