About
Endor Labs is an AI-native application security platform purpose-built for modern engineering teams working with AI coding agents and traditional development workflows alike. At its core is AURI, an agentic security engine that combines large language model reasoning with deterministic program analysis to deliver verifiable security findings backed by data flow traces, call paths, and reachability evidence — not guesses. The platform covers the full spectrum of application security needs: AI-focused SAST, AI security code reviews, secrets detection, reachability-based software composition analysis (SCA), malicious package detection, container security, artifact signing, SBOM and compliance reporting, and AI model governance. Its upgrade impact analysis and open-source reachability engine help teams understand the true blast radius of dependency changes before merging. Endor Labs integrates directly into developer workflows via Hooks, Skills, MCP, or CLI, allowing security teams to enforce policy-as-code independently from the AI coding agents generating code — ensuring security verification is never self-referential. Customers report 97.5% noise reduction, 83% fewer blocked pull requests, and 6x faster remediation times. Trusted by enterprise teams like Atlassian, Endor Labs is ideal for DevSecOps teams, security engineers, and organizations accelerating development with AI coding agents who need rigorous, trustworthy security guardrails without slowing down delivery.
Key Features
- AURI Agentic Security Engine: Combines agentic LLM reasoning with deterministic program analysis to produce verifiable findings backed by data flow, call paths, and full-stack reachability — eliminating guesswork.
- Reachability-Based SCA: Analyzes whether vulnerable open-source dependencies are actually reachable and exploitable in your codebase, cutting through noise to surface only actionable risks.
- AI Code & Agent Security: Provides an independent security verification layer for AI coding agents, enforcing policy-as-code via Hooks, Skills, MCP, or CLI without relying on the agent to check its own output.
- Secrets Detection & Malicious Package Detection: Scans code and dependencies for exposed secrets and identifies malicious packages in the software supply chain before they reach production.
- SBOM, Compliance & Container Security: Generates Software Bills of Materials, supports compliance workflows, and extends security scanning to container images with artifact signing for supply chain integrity.
Use Cases
- DevSecOps teams looking to integrate automated, low-noise security scanning into CI/CD pipelines without blocking developer velocity.
- Security engineers responsible for governing the security of AI-generated code produced by GitHub Copilot, Cursor, or other AI coding agents.
- Platform engineering teams managing open-source dependency risk at scale who need reachability analysis to prioritize vulnerability remediation.
- Compliance and risk teams needing SBOM generation, artifact signing, and audit-ready security evidence for regulatory requirements.
- Organizations adopting agentic software development workflows who need an independent, policy-enforcing security layer across all AI agents.
Pros
- Massive Noise Reduction: Customers report up to 97.5% reduction in non-actionable alerts, letting developers focus on real risks rather than sifting through false positives.
- Verifiable, Evidence-Backed Findings: Every security finding is backed by deterministic program analysis evidence — data flow, call paths, and reachability — making results auditable and trustworthy.
- Seamless AI Agent Integration: Integrates with AI coding agents via MCP, Hooks, Skills, and CLI, fitting naturally into modern agentic development workflows without disrupting velocity.
- Broad Security Coverage: Consolidates SAST, SCA, secrets detection, container security, AI model governance, and compliance into a single platform, reducing tool sprawl.
Cons
- Enterprise-Oriented Pricing: Pricing is geared toward enterprise and mid-market teams; smaller teams or individual developers may find it cost-prohibitive.
- Requires Onboarding Investment: Configuring policy-as-code, integrations, and reachability analysis across a complex codebase may require meaningful setup time from security engineers.
- Limited Public Pricing Transparency: Specific pricing tiers are not publicly listed, requiring a sales conversation to understand total cost of ownership.
Frequently Asked Questions
What is AURI?
AURI is Endor Labs' agentic security engine that combines LLM-based agentic reasoning with deterministic program analysis. It delivers verifiable security findings — complete with data flow traces, call paths, and reachability evidence — for every identified vulnerability.
How does Endor Labs handle AI-generated code?
Endor Labs acts as an independent security verification layer separate from AI coding agents. It integrates via Hooks, Skills, MCP, or CLI to review and enforce security policy on code produced by AI agents, ensuring the agent never self-verifies its own output.
What is reachability-based SCA?
Reachability-based SCA goes beyond listing vulnerable dependencies — it analyzes whether those vulnerabilities are actually reachable and exploitable within your specific application code. This dramatically reduces the number of CVEs developers need to act on.
Which CI/CD and development tools does Endor Labs integrate with?
Endor Labs integrates with a wide range of developer tools including GitHub, GitLab, and other SCM platforms, and connects to AI coding agents via MCP, Hooks, Skills, and CLI. It supports multiple languages and container ecosystems.
Is Endor Labs suitable for startups or only enterprise teams?
While Endor Labs is primarily designed for enterprise and growth-stage engineering organizations, they offer a LeanAppSec product aimed at leaner teams. Prospective customers should contact sales for pricing that fits their team size and use case.
