Open-source tool adds two-step verification to stop AI code reviewers from hallucinating

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AI-powered code review tools frequently generate false findings, flagging non-existent bugs such as null dereferences already guarded in the code or SQL injection risks handled by an ORM. The core problem is that most AI review pipelines emit findings based on pattern-matching without a second pass to verify whether an issue is actually reachable. CCA-Audit, a new MIT-licensed open-source tool, addresses this with two gates: a verification step that challenges each finding against the exact cited line, and a regression diff that confirms applied fixes change nothing beyond the intended scope. A stress test demonstrated the approach by planting one real bug alongside three decoys, with the tool successfully identifying the genuine units error while filtering out false positives. CCA-Audit integrates with Claude Code as a single command and its full agent transcripts are publicly available in the repository.

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