Review AI-generated code as a proposed change, not as verified work. Before approving it, confirm that it meets the requirement, behaves safely in its application context, and can be maintained. Tests and scanners add useful evidence, but a human who understands the change must still own the decision.
1. Establish the change’s purpose and risk
Start with the requirement, issue, or pull-request description—not the generated implementation. Establish what the change should do, who will use it, and what existing behavior must remain intact. If its purpose or behavior is unclear, ask the change owner to explain it before reviewing further.
Next, map the change to its surroundings. Inspect the changed files and the components they affect, including existing controls and dependencies. Identify the assets at stake, high-risk functionality, and trust boundaries: places where data or actions cross between users, services, privilege levels, or other security domains. OWASP’s Secure Code Review Cheat Sheet recommends understanding architecture, business requirements, threat models, previous findings, critical assets, and security requirements before examining code in detail.
- What user or business need does the change serve?
- Which files, services, data, and permissions are affected?
- What could go wrong if the change is incorrect or abused?
- Does the change involve sensitive data, authorization, concurrency, privacy, accessibility, or internationalization that calls for specialist review?
2. Check behavior against the requirement
Trace the main execution path through the changed code and compare it with the stated requirement and surrounding application behavior. Then look beyond the happy path: inspect invalid and boundary inputs, failure handling, authorization decisions, state changes, and concurrency where relevant. Consider the experience of the person using the feature, not just whether the implementation runs.
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Assess whether tests exercise the behavior at an appropriate level—unit, integration, or end-to-end—and whether their assertions would detect a broken implementation. Ask two useful questions: would this test fail if the code were wrong, and could a future change make it pass without preserving the intended behavior?
Review test changes as code
Generated tests are not independent proof of generated behavior. Check whether tests were removed, assertions weakened, or mocks substituted for the real unit or dependency whose behavior needs verification. Look for tests that merely encode what the implementation does instead of demonstrating that it matches the requirement. As Google’s code-review guidance puts it, “Tests do not test themselves, and we rarely write tests for our tests—a human must ensure that tests are valid.”
Where the risk warrants it, add or request negative, adversarial, boundary, malformed-input, and concurrency cases. A passing suite written or altered in the same generation loop is evidence to inspect, not independent assurance.
3. Inspect security properties and data flow
Begin at entry points and trust boundaries, then trace untrusted input into sensitive operations. Check how the change handles interpreters, database queries, file paths, network requests, deserialization, and other destinations where unsafe input can cause harm. Verify validation and safe encoding in the relevant context rather than assuming that a generic check is sufficient.
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Review authentication and authorization separately: proving who a user is does not prove they may perform a particular action. Follow sensitive data through storage, transmission, logs, and errors; check cryptographic use, secure defaults, configuration, and business logic. Consider new attack paths and security regressions, not only familiar vulnerability patterns. OWASP’s manual-review guidance explains why automated analysis needs context: application logic, data flow, and system-specific flaws require human understanding.
Check dependencies and agent-related changes
For any new or changed dependency, check maintained vulnerability information and the project’s established policy. Do not assume a generated package name or version is current or safe. Inspect changes to configuration, CI/CD, permissions, and any tool access granted to an AI agent. OWASP’s Secure Coding with AI Cheat Sheet calls out risks including outdated or hallucinated dependencies, indirect prompt injection in agent workflows, excessive permissions, and test tampering.
4. Use independent checks that fit the risk
Choose verification methods based on the code, its exposure, the assets it affects, and its deployment context. NIST’s developer-verification guidance describes a range of techniques, including threat modeling, automated testing, static code scanning, heuristic secret detection, built-in checks, black-box and structural tests, historical tests, fuzzing, web-application scanners where applicable, and review of included libraries, packages, and services.
These methods answer different questions; no one check establishes that a change is correct or secure. Use the options that address the change’s actual risks, and interpret results in context.
| Method | Useful for | Limit to keep in mind |
|---|---|---|
| Human review | Intent, architecture, business logic, data flows, and context-specific decisions. | Depends on reviewer expertise and available time. |
| Automated tests | Repeatable checks of specified behavior. | Value depends on whether test cases and assertions genuinely represent requirements. |
| Static and dependency analysis | Code patterns and known risks in components. | Does not establish correct business behavior or rule out every vulnerability. |
| Dynamic, web, fuzz, and property-based tests | Runtime behavior under selected inputs and conditions. | Need suitable environments, threat models, and targeted cases. |
For high-risk changes, consider an independent security review and tests designed outside the same generation loop. The OWASP AI Security Verification Standard Appendix C recommends qualified human review of AI-generated code and automated security testing on relevant pull requests. It also identifies differential fuzzing or property-based tests for security-critical input validation, authorization, and deserialization behavior. Treat these as verification options, not a guarantee from using a checklist or scanner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Assess maintainability and fit
Ask whether another developer can understand and safely modify the change later. Check that its design and abstractions fit the problem and the existing system; look for unnecessary complexity, over-generalization, unclear names, misleading comments, and style inconsistencies. Review whether the tests will help preserve intended behavior and whether documentation needs to change because a user or developer workflow changed.
Google’s review guidance covers design, functionality, complexity, tests, naming, comments, style, and documentation. Use those concerns to find substantive problems, not to demand perfection: resolve meaningful security, behavior, and maintainability issues before approval without blocking a sound change over minor polish.
6. Keep review proportional and approval accountable
A small incremental change and a major release do not require identical review effort. OWASP distinguishes diff-based review from baseline reviews of whole systems or major releases; both still depend on architecture and risk context. Increase scrutiny when a change affects critical assets, expands exposure, alters security controls, or introduces complex behavior.
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