Synergizing Static Analysis with Large Language Models for Vulnerability Discovery and beyond

Abstract: This report examines the synergy between LLMs and Static Application Security Testing (SAST) to improve vulnerability discovery. Traditional SAST tools, while effective for proactive security, are limited by high false-positive rates and a lack of contextual understanding. Conversely, LLMs excel at code analysis and pattern recognition but can be prone to inconsistencies and hallucinations. By integrating these two technologies, a more intelligent and efficient system is created. This combination moves beyond mere vulnerability detection optimization, transforming security into a deeply integrated, contextual process that provides tangible benefits like improved triage, dynamic bug descriptions, bug validation via exploit generation and enhanced analysis of complex codebases. The result is a more effective security approach that leverages the strengths of both technologies while mitigating their weaknesses. SAST-Genius reduced false positives by about 91 % (225 to 20) compared to Semgrep alone.