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You do not need to be a security specialist to review AI-generated code more carefully. Compare the change with the requested work, inspect the entire diff, trace data and permissions, verify dependencies and tests, and run the project’s usual checks. Treat scanners and passing tests as useful evidence—not proof that the code is safe. Ask an experienced reviewer to help when a change affects a security boundary or you cannot confidently explain what it does.
Start with the requested change, not the AI’s explanation
Before reading individual lines, restate what the change is meant to do. Compare the diff with the issue, acceptance criteria, or design, and check whether it fits the project’s conventions. A change can look polished and still solve the wrong problem or introduce behavior nobody requested. GitHub’s guide recommends evaluating generated code in the context of the task, rather than relying on plausibility alone: Review AI-generated code.
Write down the expected behavior in plain language. For example: “A signed-in user can update their own display name, but cannot change another user’s account.” That gives you a concrete question to test against the code, including its authorization checks.
Read the complete diff, file by file
Do not approve based only on an AI agent’s summary, a pull-request description, or the main source file. Review every added, modified, and deleted file—including files that often seem routine. OWASP warns that generated changes can include unrelated edits and that reviewers should inspect the full result: Secure Coding with AI Cheat Sheet.
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- Application code: Look for new behavior and changes to existing behavior.
- Tests: Check additions, edits, and deletions; a removed test may matter as much as a new one.
- Dependency manifests and lockfiles: Identify packages added, removed, or upgraded.
- Configuration and CI: Check deployment settings, permissions, build steps, and security controls.
- Agent instruction or rules files: Notice changes that could affect future tool behavior or review assumptions.
Investigate changes outside the task’s stated scope. If a file changed and you cannot explain why, ask before approving.
Trace data, permissions, and security boundaries
For each important changed path, follow the data: where it comes from, how the program handles it, and where it ends up. Then ask who is allowed to perform the operation. OWASP’s secure code review guidance emphasizes manual review for business logic and context-specific flaws that automated tools may not understand: Secure Code Review Cheat Sheet.
Rank #2
- Input validation: Can unexpected, malformed, or oversized input reach sensitive operations?
- Output handling: Is user-controlled data safely handled before it is rendered, logged, or passed to another system?
- Authentication: Does the code establish who the user is where that matters?
- Authorization: Does it check that this user may perform this specific action on this specific resource?
- Secrets: Are credentials or tokens exposed in code, logs, tests, or configuration?
- Security-sensitive configuration: Did the change weaken access restrictions or alter deployment protections?
These questions are prompts to investigate, not a substitute for understanding the application’s design. If you cannot tell whether a data flow or permission check is correct, flag the uncertainty rather than assuming the code is safe.
Verify dependencies independently
Do not assume a generated package name is real, suitable, current, or safe. For each dependency change, confirm that the package exists in the intended ecosystem, is appropriate for the project, has a compatible license, and is not known to have a vulnerability. Use the project’s normal dependency audit process or an appropriate scanner. OWASP specifically cautions that AI may suggest hallucinated or outdated dependencies; GitHub’s review guidance also recommends checking generated changes rather than accepting them at face value.
Review tests as code
A passing test suite shows that the executed tests passed; it does not show that the tests encode the right behavior or cover the security-relevant cases. Inspect test changes for weakened assertions, removed cases, or mocks that replace the behavior the test is supposed to verify. Where it matters, add or request tests for invalid input and important edge cases.
Ask what the tests actually prove. A test that checks an endpoint returns a successful response may not establish that one user cannot access another user’s data. Test results are useful only in relation to the behavior and boundaries they exercise.
Rank #4
Run project checks and understand their limits
Build or compile the change, run relevant tests, review warnings, and use the static-analysis and dependency checks already available in the project. GitHub recommends tests and static analysis as part of reviewing AI-generated code; OWASP recommends human review alongside security tooling. Keep a clear record of what ran and what did not.
| Review method | Useful for | Does not establish by itself |
|---|---|---|
| Human review | Understanding task context, project conventions, business logic, and security boundaries | That every defect or vulnerability has been found |
| Automated checks | Finding known patterns, build failures, test failures, and dependency issues at scale | That the change meets the intended behavior or is secure in its application context |
These methods complement one another; neither a clean scan nor a human pass is a guarantee. If a check could not run, say so plainly rather than treating it as a pass.
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Consider what the coding agent saw and could do
Issue descriptions, comments, documentation, logs, and fetched web pages can contain untrusted content. If an agent processed them, inspect its resulting diff for unrelated changes or weakened controls. When possible, limit the agent’s access to what the task requires and avoid exposing credentials or sensitive files to unnecessary context. OWASP’s Secure Coding with AI Cheat Sheet discusses risks associated with AI-assisted coding and the need to review the resulting changes.
Know when to request an expert review
Ask a reviewer with relevant security experience when the change touches authentication, authorization, cryptography, sensitive data, deployment configuration, or another boundary you cannot confidently assess. The same applies when the change is difficult to understand or its consequences are unclear. OWASP’s Top 10:2025 makes the responsibility explicit: “You are responsible for all code that you commit.” OWASP Top 10:2025, Next Steps.
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