AI-Native SAST

SAST that thinks like a security engineer

Intelligent static analysis that understands how your code works and what matters to your organization. Know what’s exploitable, what’s not, and how to fix it.

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AI-Native Static Application Security Testing (SAST)

How it works

1

Cut through the noise

Automatically triage false positives using AI code analysis, including multi-file and multi-function dataflow validation.

2

Identify complex flaws

Go beyond traditional rule-based scanning to detect complex vulnerabilities like business logic and authentication flaws.

3

Fix issues at the source

Integrate directly into AI code editors to help developers and agents fix code before their first commit.

Securing code written by humans and AI at:

Software analysis is hard, and there's only one company [Endor Labs] that's doing it correctly.”

‍

Paul Padilla

Head of Software and Infrastructure Security, Mysten Labs

Prioritize

Zero in on real security issues

Skip the manual research. Agents auto-triage findings by parsing syntax, tracing dataflow, and reasoning about context and logic so you only see issues that actually matter.‍

  • ‍Reduce false positives: Agents validate findings and provide transparent evidence and reasoning for every decision.‍
  • Triage at scale: Drive the right action at the right moment using the Endor Labs’ policy engine—no more clicking through noisy finding feeds.‍
  • Adapt to your environment: Add custom prompts and rules to align agent behavior with your security policies, priorities, and threat models.
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Identify 

Detect auth and business logic flaws

Find complex logic flaws, broken access control, and other risks typically found in pentest reports and bug bounty programs.

  • Scalable coverage: Detect risks across your codebase without the overhead of rule creation and upkeep.
  • Catch risky changes early: Detect when pull requests alter your security posture in ways that could introduce exploitable logic flaws.‍
  • Ready for modern threats: Identify prompt injection and other LLM security issues without writing new rules.
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Remediate

Fix code with context

Focus on high-signal, reachable findings and deliver precise, explainable fixes—right where developers work.

  • Fix at the source: Guide developers and coding agents to remediate issues directly within AI code editors, with full understanding of the surrounding logic.
  • Get smart fix suggestions: Generate context-aware fixes aligned with your codebase and ready for developer review.‍
  • Verify with confidence: Each finding includes the exact snippet, triggered rules, CWE reference, agent reasoning, and recommended fix—so every change is traceable and trusted.
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AppSec for The Software Development Revolution

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