AI SAST: Code Security for the Agentic SDLC | Endor Labs

Summary

Endor Labs has published a whitepaper on its AI SAST product, which pairs deterministic program analysis with agentic reasoning to find and fix security flaws in AI-written code. The source reports that in benchmark testing only 10.5% of solutions from an AI coding agent were secure, and that the product found 192 real vulnerabilities on a ground-truth benchmark against 4 traditional SAST tools and 2 frontier models, more than double any other tool. The whitepaper also covers how the pipeline runs from code graph to proposed fix and how findings keep a stable identity across scans.

Topics

• AI agents