Endor Labs AI SAST
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Orchestrates detection, triage, and remediation agents on a semantic code model rather than pattern matching; in Endor Labs' June 2026 benchmark across eight real-world projects it found 192 real vulnerabilities (around 3x what frontier models found), with 4x more high-severity findings than the str
AI-based static application security testing that builds a code context graph and semantic index of an entire codebase, uses specialized agents to find flaws, triage findings, and score severity; incorporates IaC, repository context, user feedback, and architecture/threat model context to improve ac
Static application security testing tool; per Endor Labs' AI SAST benchmark across real open-source projects, found 2x more real vulnerabilities than any other tool tested, including 63 that no other tool caught
Runs pattern-based detection then uses AI agents to triage findings, trace dataflow source-to-sink, check sanitizers/controls, classify true/false positives with evidence; works across 40+ languages; found 2.6x more real vulnerabilities than frontier AI models per Endor Labs' AI SAST benchmark
AI-driven static application security testing engine that traces data flows from untrusted input sources to dangerous sinks (e.g., unbounded allocations) to identify vulnerabilities like this one in memory-safe languages such as Rust.
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