AI SAST: Code Security for the Agentic SDLC

AI-generated code is becoming common in production software, while security tools face limitations such as high false-positive rates in pattern-based SAST and limited code coverage by frontier AI models. Endor Labs’ whitepaper presents its AI SAST approach, which combines deterministic program analysis with agentic reasoning to identify vulnerabilities more effectively. It reports finding 192 real vulnerabilities in a ground-truth benchmark against four traditional SAST tools and two frontier models, more than twice as many as any competing tool. The paper also describes a pipeline using structured code graphs for detection, triage, and proposed fixes, along with controls intended to maintain stable, auditable findings despite LLM non-determinism.

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