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Review AI-generated code the way you would any proposed change: verify what it is meant to do, test that behavior, inspect security boundaries and dependencies, and decide whether another developer can safely maintain it. The fact that an AI produced a plausible patch—or that tests and scanners pass—does not establish that it is correct or secure. A human owner should understand and approve every change before it is merged.
Start with intent and the surrounding code
Before running checks, establish what the change is supposed to accomplish. Read the request, acceptance criteria, and relevant neighboring code. Compare the patch with the project’s architecture and conventions, and identify assumptions about users, inputs, business rules, and failure behavior. Check that the change solves the requested problem rather than merely producing plausible output, and look for unrelated edits.
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Review the change in context: callers and callees may rely on invariants that are not visible in the edited file. GitHub’s guidance for reviewing AI-generated code recommends checking functionality and context, including whether generated code has ignored constraints, used nonexistent APIs, or removed or skipped tests.
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Verify behavior with builds and tests
Build or compile the project, run the existing test suite, and inspect warnings and failures. Add or examine tests for the changed behavior, including relevant boundary values, error paths, and interactions with callers. A test can give false confidence if it simply repeats the implementation’s assumptions, so check that it represents the intended behavior independently.
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- Confirm tests were not deleted, disabled, or skipped to make a failing change appear green.
- Check what happens with empty, malformed, unexpected, or unusually large inputs where those cases apply.
- Exercise failure behavior: errors should be handled deliberately rather than silently ignored or exposed inappropriately.
- Use the testing method that fits the behavior: unit or structural tests, black-box or end-to-end tests, and fuzzing reveal different kinds of defects.
NIST’s 2021 Guidelines on Minimum Standards for Developer Verification of Software describes complementary techniques including threat modeling, testing, static scanning, secret detection, fuzzing, web application scanning where applicable, and checks of included code such as libraries and services. These techniques improve verification; none guarantees that every defect will be found.
Review security boundaries, not just suspicious-looking lines
Trace untrusted input through the changed code to the sensitive operations it can influence. Ask what changed about the trust boundary and what an attacker could control. Authentication (who a user is) and authorization (what that user may do) are separate checks; confirm both are appropriate to the operation and its callers.
- Look for missing authorization or validation, injection risks, unsafe deserialization, and mishandled file uploads.
- Inspect secrets, cryptographic choices, error handling, and changes to public endpoints, integrations, storage, CORS, or network exposure.
- Check query construction and data flow into sensitive operations, not merely whether input is validated somewhere in the code.
- For authentication, authorization, cryptography, parsing, or other high-risk paths, involve a trained reviewer or security champion.
OWASP secure code review guidance emphasizes risk-based review and context: scanners often miss broken access control and business-logic flaws, and a green scan does not show that those flaws are absent.
Check dependencies and build-related changes
For each added or updated package, verify that the package actually exists, comes from a legitimate source, is maintained, and has a license compatible with the project. Generated code can suggest a package name that does not exist; an attacker may register a matching name. Confirm the intended package rather than trusting the spelling in an import statement.
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When the patch touches them, inspect lockfiles, package scripts, build configuration, CI workflows, and third-party actions as part of the change. GitHub’s review guidance and the OWASP Secure Coding with AI Cheat Sheet both call attention to dependency verification.
Assess whether the change will be maintainable
Read the patch as the person who will need to change it later. Check whether names and control flow are understandable, comments clarify non-obvious decisions, and the design matches local conventions and the scale of the problem. Look for unnecessary duplication, complexity, or abstractions, and ask whether the code is divided into focused, testable units.
Passing tests does not by itself show that a future developer can understand the implementation or safely extend it. Automated quality checks may flag patterns, but a reviewer must judge whether the design fits this codebase. GitHub’s review guidance includes maintainability and project context as parts of assessing generated changes.
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Run the checks appropriate to the project: automated tests and static analysis, dependency and secret scanning, and—where the application warrants them—web application scanning or fuzzing. These tools are useful for repeatable checks and known classes of problems. They do not replace review of business logic, access control, architecture, or assumptions that depend on the surrounding system.
GitHub states in its Copilot inline suggestions safety guidance: “While inline suggestions can generate syntactically correct code, it may not always be secure.” The same distinction matters in review: syntax, a passing build, or a clean scanner report is not a security verdict. AI-generated review comments can help identify questions to investigate, but they are not proof either.
Scale the review to the risk and the tool’s permissions
Review every change, then give extra scrutiny to changes involving authentication, authorization, cryptography, input parsing, deserialization, file uploads, public endpoints, integrations, data stores, CI/CD, or infrastructure. A small-looking patch can still alter a security boundary through its callers, configuration, or dependencies.
Inline suggestions primarily propose edits. Coding agents may also run commands, access networks, modify multiple files, or use credentials, so their permissions create additional risks beyond the code they produce. Keep permissions limited, sandbox execution, require approval for consequential actions, and scrutinize repository instruction files and newly introduced tools. See the OWASP guidance on IDE and AI-assisted development security and the OWASP Secure Coding with AI Cheat Sheet.
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Assign a human owner who can explain what the change does and why it is safe and maintainable. Require human review and approval before merging. AI authorship, generated test results, or an AI system’s approval does not transfer responsibility for the code.
The OWASP Secure Coding with AI Cheat Sheet calls for a human owner for each AI-assisted change. Use that ownership to ensure unresolved questions are answered, risky paths receive qualified review, and the final change is understood by the people responsible for the project.
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