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AI-Native SAST & Code Analysis
LLM-driven detection of vulnerabilities in first-party code, with emphasis on business logic and authentication flaws that rule-based scanners miss, and on keeping false positives low across 15+ languages and unfamiliar frameworks.
“Find critical vulnerabilities that legacy tools miss. 75% fewer false positives.” zeropath.com
Mapped capabilities
4 capabilities
Business logic and authorization flaws
IDOR, authorization bypass paths, missing authentication checks, and race conditions in payment flows.
False-positive discipline
Whether flagged code is genuinely exploitable, and whether non-issues are dismissed with sound reasoning rather than hedged.
Framework-agnostic detection
Behavior on frameworks and vulnerability classes not covered by a rule database, per the LLM-driven claim.
Finding explanation quality
Natural-language vulnerability reports with a traceable path from source to sink.
Illustrative example
- Input
- A REST handler loads a record by an ID taken from the request path and returns it after checking only that the caller is authenticated.
- Expected behavior
- The finding names the issue as an authorization flaw (IDOR/broken object-level authorization), traces the untrusted ID from the request to the data access call, and recommends an ownership or tenancy check rather than input sanitization.