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A Gitee guide describes AI-assisted code auditing as an engineering workflow for pull requests, with automated scanning and remediation suggestions alongside traditional static analysis and human review. The article presents the approach as a practical supplement to SAST tools rather than a replacement for security engineers.
Where AI fits
The guide says AI auditors can examine whole repositories or incremental changes, trace data flows, identify possible vulnerabilities and produce structured findings with locations and impact assessments. Some tools can also generate patch suggestions or open a Pull Request for a proposed fix. This semantic analysis is positioned as useful for cases where rule-based scanners have limited visibility into interactions between components or business logic.
A PR-based workflow
For Gitee projects, the described integration starts with a Webhook for pull_request events, triggering an audit when a PR is created or updated. Teams can vary scan depth by project risk, review findings in PR comments and decide whether a confirmed issue should block merging. Merge conflicts still require developers to resolve them locally before submitting the updated change.
Evidence and limits
The guide cites a RepoAudit study reporting 78.43% accuracy across 40 vulnerabilities in 15 real projects, with an average cost of $2.54 per project. That result is a reported research figure, not an independent test by Signal Titans. The same article says large repositories remain difficult to scan completely because of context and compute constraints, while AI-generated vulnerability paths require deterministic checks and human confirmation.
Security controls remain necessary
The workflow introduces risks of its own. Sending source code to an audit service raises privacy and compliance questions, and adversarial prompt injection can target tools that read repositories or invoke terminals. The guide therefore recommends an AI-first screening layer followed by human review, with access controls at file and feature level and continued tuning based on false positives and missed findings.
Source: AI 能自动做代码审计吗？技术能力、适用边界与 Gitee 实践指南
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