Endor Labs

Endor Labs is a cybersecurity tool. Endor Labs AI SAST uses coordinated AI agents plus program analysis to find access control, IDOR and authentication flaws that rule-based scanners miss, triaging every finding automatically and pairing confirmed vulnerabilities with codebase-aware fixes.

Endor Labs AI SAST is a static application security testing product built around a different premise than conventional scanners. Rule-based tools match patterns; AI SAST orchestrates several specialized agents that read code, trace how user-controlled data moves between files and functions, and reason about how the application actually behaves. That lets it surface categories pattern matching has historically missed: IDOR, broken access control, insecure design, fail-open authentication and missing validation at trust boundaries. The vendor reports benchmark results of roughly 2x the findings of rule-based SAST and 3x those of frontier models, and cites OWASP data showing broken access control appearing in every application tested. The triage problem gets equal attention. The page states that legacy SAST produces 68 to 78 percent false positives on average, with 15 to 30 minutes of engineer time spent on each. AI SAST classifies every finding as true positive, false positive or unknown and attaches a written explanation that teams can audit. Policies built on OPA let security leads suppress noise, gate pull requests only on high-confidence results, or route findings by owning team. Confirmed vulnerabilities arrive with a codebase-aware remediation and the evidence behind it, aiming to cut the research time per finding from hours to minutes. Architecturally, the product reasons over Endor Labs' code context graph rather than pushing an entire repository through a model on each run. The comparison the vendor draws is against pointing Claude or Codex at a repo directly: on a large Java codebase, those models examined under 10 percent of the security-relevant code. Pre-computed program analysis is meant to keep coverage from competing with cost. It suits AppSec teams and platform security engineers at organizations with large, multi-service codebases, particularly shops where developer trust in the scanner has eroded. It plugs into CI/CD pipelines and language and tooling ecosystems, and it competes with established SAST platforms rather than lightweight linters. Bug bounty and security research teams also have a use case: the vendor documents AI SAST discovering a zero day in a third-party library.