Review an AI-generated patch as an untrusted contribution: check every changed file against the intended behavior, inspect tests and security-sensitive configuration, run project-appropriate checks, and apply or merge it only when a human reviewer understands and approves the result. A generated explanation or passing test run is not enough on its own.
1. Define what the patch is meant to change
Before reviewing code, write down the expected behavior, intended files or interfaces, and relevant project conventions. Use that as a contract: a change can be syntactically valid and still solve the wrong problem or conflict with the repository’s architecture. Check nearby callers and tests when the patch may affect them. GitHub’s guidance on reviewing AI-generated code likewise emphasizes whether generated code fits the project’s purpose, architecture, and conventions.
2. Inspect the complete diff, file by file
Read the diff itself rather than relying on an AI-generated summary. Check every changed file, including files that seem peripheral to the requested feature. Scope drift can hide in a lockfile, test, build script, CI workflow, or deployment manifest. OWASP advises reviewers to review every file in an agent-generated pull request individually and look for unexpected modifications.
- Confirm each edit is necessary for the requested behavior.
- Look for unrelated refactors, new dependencies, or generated files that were not expected.
- Check whether configuration changes alter permissions, build behavior, or deployment.
- Follow changed data and control flow through callers, error handling, and access checks.
3. Review behavior and tests independently
Manually trace important paths, including invalid input, boundary conditions, failures, and permission checks. Automated analysis is useful, but it can miss contextual logic flaws; OWASP describes secure code review as manual examination for vulnerabilities automated tools often miss.
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Treat tests as part of the patch
Tests are code under review, not proof that the implementation is correct. Investigate deleted tests, weakened assertions, mocks that bypass the behavior in question, and tests that simply encode the generated implementation. Where relevant, add or require independently designed cases for negative inputs, boundaries, and concurrency. OWASP warns that a passing suite generated by the same agent that produced the code provides no independent assurance.
4. Run checks that match the change
Select checks based on the affected behavior and the repository’s supported tooling. Depending on the project, that can include compilation or type checking, unit and integration tests, end-to-end tests, linting, static analysis, dependency review, and secret scanning. GitHub recommends running automated tests and static analysis; examples in its guidance include CodeQL and Dependabot. NIST verification guidance also covers approaches such as threat modeling, static scanning, secret heuristics, structural and black-box testing, fuzzing, and dependency checks.
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A green result means only that the checks you ran passed under their configured conditions. It does not establish that the behavior is right, that test coverage is adequate, or that the patch is safe in its actual context. Pair automation with human review.
5. Give automatically executed files extra scrutiny
A small change to a file that runs during installation, building, CI, or deployment can have broad consequences because it may execute in a trusted environment. Inspect package lifecycle scripts, shell commands, Docker and build files, workflow definitions, deployment manifests, and generated scripts carefully.
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- Check added commands, downloads, and network access.
- Review third-party action references and the permissions granted to workflows.
- Look for ways credentials or secrets could be exposed.
- Verify that an install or setup command does only what the project requires.
OWASP specifically cautions against blindly pasting and running installation commands generated by an agent, which could execute malicious code.
6. Apply the patch against the actual repository state
There is no single safe apply command for every workflow: the right operation differs for a pull request, commit, or patch file. Use the repository’s normal process, and first establish which branch and working-tree state you are changing.
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- Confirm the target branch and inspect the current working tree so you know whether unrelated local changes are present.
- Review the exact patch or proposed changes before applying them through the project’s usual mechanism.
- Inspect the resulting diff after application to confirm it matches what you intended and contains no unexpected changes.
- Rerun the checks needed for the resulting repository state before merging or deploying.
7. Require a human owner and approval
A qualified developer must be able to explain and take responsibility for the final change, including its security and maintenance implications. An AI summary, an AI reviewer, or an AI-generated test suite cannot replace that ownership. Require explicit human approval before merging.
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