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Start with intent, not the code’s appearance
Before judging implementation details, read the request, issue, acceptance criteria, and nearby code. Establish what the change is supposed to do, which users and inputs it affects, and how it should behave when something goes wrong. Then compare the patch with that expectation and with the project’s existing architecture and conventions.
- Does the change solve the requested problem, rather than merely produce plausible output?
- Does it honor constraints and business rules that may be described outside the changed function?
- Are there unrelated edits that should be separated or removed?
- Do callers and neighboring components rely on assumptions this patch could break?
GitHub’s guidance on reviewing AI-generated code calls out mistakes such as hallucinated APIs, ignored constraints, incorrect logic, and deleted or skipped tests. These are reasons to inspect the whole change and its context, not just whether it compiles.
Verify behavior and edge cases
Build or compile the project, run its existing tests, and inspect or add tests that exercise the changed behavior. A test that repeats the implementation’s assumptions can pass while the underlying behavior is wrong, so check the expected result independently against the request and project rules.
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- Cover ordinary inputs as well as boundary values, malformed input, empty values, and unexpected states.
- Trace error paths and interactions with callers, not only the happy path.
- Inspect build warnings and investigate tests that have been removed, disabled, or skipped.
- Use unit, integration, end-to-end, or fuzz testing according to the behavior and attack surface; no single test type exposes every failure mode.
Review security boundaries and data flow
Identify what changed about trust: what data or actions can an attacker control, and where does that input go? Follow untrusted data through the patch into sensitive operations. Check authentication and authorization separately: a user may be authenticated yet still lack permission to perform a particular action.
- Check that validation happens at the appropriate boundary and that access control is enforced for each relevant operation.
- Inspect query construction, deserialization, file uploads, secrets handling, cryptography, and error responses.
- Look for changes to public endpoints, integrations, storage, CORS settings, or network exposure.
- Consider both callers and callees: a local change can violate a security invariant enforced elsewhere.
OWASP’s secure code review guidance emphasizes risk-based review. Scanners can identify repeatable patterns, but context-dependent problems such as broken access control and business-logic flaws may escape them. A green scan is not proof that a change is safe. Route high-risk paths to a trained reviewer or security champion.
Verify packages, build files, and agent permissions
For every added or updated dependency, confirm that the package exists, comes from a legitimate source, is maintained, and has a license compatible with the project. AI-generated suggestions can include plausible but nonexistent package names; a matching name may be registered by someone else. Review lockfile changes as well as the manifest.
When the patch touches build configuration, package scripts, CI workflows, or third-party actions, inspect those changes as part of the security review. If an AI coding agent produced the patch, account for its capabilities too: tools that can run commands, access networks, modify files, or use credentials create risks beyond those of inline code suggestions. OWASP’s Secure Coding with AI Cheat Sheet and IDE and AI-assisted development guidance recommend limiting permissions, sandboxing execution, and requiring approval for consequential actions. Treat repository instruction files and newly introduced tools as part of the review surface.
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Assess maintainability as well as correctness
Read the patch as the person who will have to change it next. Passing tests do not show whether the design is understandable or whether a future edit can be made safely.
- Are names and control flow clear, and are comments useful and accurate?
- Does the code follow local patterns without introducing needless complexity or duplication?
- Are functions and abstractions focused enough to test and reason about?
- Can the change be divided into smaller, understandable units if it currently combines unrelated work?
Automated quality checks may flag duplication or complexity, but a reviewer must decide whether the design fits the codebase and scale of the problem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Combine automation with contextual review
Use automated checks to make repeatable verification more consistent, then interpret their results in context. A practical baseline is:
- Builds and automated tests for the changed behavior.
- Static analysis and code-quality checks on the patch.
- Dependency and secret scanning when relevant to the project.
- Web application scanning or fuzzing when the code and attack surface justify them.
NIST’s Guidelines on Minimum Standards for Developer Verification of Software, published in 2021, describes complementary techniques including threat modeling, black-box and structural tests, historical tests, static scanning, secret detection, fuzzing, and checks of included code such as libraries and services. Choose techniques to fit the change; none guarantees that every defect will be found.
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Human review and automation serve different purposes. Automation is useful for known patterns, repeatable test execution, secrets, and dependency checks. A human reviewer is needed to assess intent, business logic, authorization context, and fit with the architecture. GitHub’s guidance on Copilot inline suggestions makes the distinction plainly: “While inline suggestions can generate syntactically correct code, it may not always be secure.”
Scale scrutiny to risk, but review every change
All changes warrant review. Spend more time and involve qualified reviewers when a patch touches authentication, authorization, cryptography, input parsing, deserialization, file uploads, public endpoints, integrations, data stores, CI/CD, or infrastructure. A small diff can still change a critical trust boundary.
Inline suggestions primarily propose edits; agentic tools may execute commands, use tools, access networks, and alter multiple files. For agent-produced changes, check both the code and the permissions or actions used to produce it. OWASP’s AI-assisted development guidance supports reviewing generated changes and applying security checks regardless of how the code was authored.
Make human ownership part of the merge decision
Assign a human owner who can explain what the change does, why it is needed, and how its behavior and risks were checked. Require that owner’s review and approval before merging. AI authorship, AI-generated review comments, passing tests, or a clean scan do not transfer responsibility for the change.
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