Review AI-generated code like any other proposed change: verify that it meets the intended behavior, inspect its security impact and dependencies, assess whether the code can be maintained, and require a human to understand and approve it before merging. Passing tests or a clean scanner result helps, but neither proves the change is correct or safe.
Start with the intended behavior and project context
Read the issue, request, or acceptance criteria alongside the changed code and its neighbors. Ask whether the patch solves the actual problem, follows the project’s conventions, and respects assumptions about users, inputs, business rules, and failure cases. Look for unrelated edits that may have arrived with the requested change.
Context matters: a line can look reasonable in isolation yet break an invariant enforced elsewhere. Trace how callers use the changed code and what the changed code expects from its callees. GitHub’s guidance on reviewing AI-generated code recommends checking functionality and context, including for hallucinated APIs, ignored constraints, incorrect logic, and tests that were deleted or skipped.
Verify behavior before judging the patch by appearance
- Build or compile the project. Investigate errors and warnings rather than treating a successful build as proof of correctness.
- Run the relevant existing tests. Check what they actually exercise, including changed callers and interactions with surrounding components.
- Add or inspect tests for the new behavior. Cover boundary values, error paths, unusual but valid inputs, and relevant failure modes.
- Question the assumptions behind the tests. A test that merely repeats the implementation’s assumptions can pass while the behavior remains wrong.
- Investigate removed, disabled, or skipped tests. Determine why they changed; suppressing a failing test is not a fix.
Choose the test approach to fit the change. Unit and structural tests can check local behavior; black-box or end-to-end tests exercise behavior across boundaries; fuzzing can help explore input-heavy paths. NIST’s Guidelines on Minimum Standards for Developer Verification of Software, published in 2021, describes complementary techniques including threat modeling, black-box and structural testing, historical tests, and checks of included code such as libraries and services. No single technique establishes that every defect has been found.
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Review security boundaries and attacker-controlled data
Trace untrusted input through the changed code to sensitive operations. Ask what trust boundary changed and what an attacker could control. Authentication and authorization are separate checks: confirm both that a user’s identity is established where needed and that the user is permitted to perform the specific action.
- Check validation at the right boundary, as well as query construction, deserialization, and file-upload handling.
- Inspect secrets, cryptography, and error handling for exposure or unsafe behavior.
- Look for changes to public endpoints, integrations, storage, CORS, or network exposure.
- Trace relevant callers and callees to see whether the patch breaks a security invariant enforced elsewhere.
Give deeper scrutiny to changes involving authentication or authorization, cryptography, input parsing, deserialization, uploads, public endpoints, data stores, integrations, CI/CD, or infrastructure. OWASP’s secure code review guidance emphasizes risk-based review and warns that scanners may miss context-dependent access-control and business-logic flaws. Route high-risk changes to a trained reviewer or security champion.
Verify dependencies and build-system changes
For every added or updated package, confirm that the package exists, comes from a legitimate source, is maintained, and has a license compatible with the project. AI-generated suggestions can name packages that do not exist; an attacker may register a matching name. Review lockfile changes alongside the manifest so you understand what will actually be installed.
When the patch touches build configuration, inspect package scripts, CI workflows, and third-party actions as well as application code. GitHub’s review guidance and OWASP’s Secure Coding with AI Cheat Sheet both call attention to dependency verification.
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Assess whether the change will be maintainable
Read the patch as the person who will need to change it later. Check that names and control flow are understandable, comments explain non-obvious decisions, and the design fits local conventions and the scale of the problem.
- Look for needless duplication, complexity, or abstractions that make a small change harder to follow.
- Check whether functions and responsibilities are focused enough to test and change safely.
- Confirm the change is organized into understandable units rather than a broad, opaque patch.
Passing tests does not show whether future maintainers can understand the behavior or extend it safely. Automated quality checks may flag some concerns, but a reviewer must decide whether the design fits this codebase.
Use automated checks as evidence, not a verdict
A useful baseline is automated tests and static analysis, plus dependency and secret scanning. Add web application scanning or fuzzing where the application and changed attack surface make them relevant. NIST’s verification guidance also includes threat modeling and checks of included code; these methods complement one another rather than certify a patch.
Scanners are useful for repeatable classes of problems, but a green result cannot establish that business rules, authorization, or data flows are correct. GitHub’s official guidance for Copilot inline suggestions puts the distinction plainly: “While inline suggestions can generate syntactically correct code, it may not always be secure.” Treat AI-generated review comments the same way: investigate them, but do not treat them as approval or proof.
Adjust review depth to the tool and the risk
Review every change, then scale the depth to what it can affect. An inline suggestion mainly proposes an edit. A coding agent may also run commands, access networks, modify multiple files, or use credentials, so its permissions and actions become part of the review.
For agentic tools, keep permissions limited, sandbox execution, and require approval for consequential actions. Inspect repository instruction files and newly introduced tools, since they can shape how an agent behaves. OWASP’s IDE and AI-Assisted Development Security guidance and AI security cheat sheet address these additional risks.
Keep human ownership explicit
Assign a human owner who can explain what the change does and why it is acceptable. Require human review and approval before merging. The person who approves the change remains accountable for its security and maintainability; AI authorship or an AI-generated approval does not transfer that responsibility. OWASP’s Secure Coding with AI Cheat Sheet calls for a human owner for every AI-assisted change.
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