You can review AI-generated code more safely without being a security specialist by checking it against the request, reading the entire diff, tracing important data and permissions, verifying dependencies and tests, and running the project’s normal checks. These steps reduce the chance of missing a problem; they do not prove a change is secure. Ask an experienced reviewer to look at high-impact or hard-to-understand changes.
What to look for when reviewing AI-generated code
Start with the change itself, not the agent’s summary. A plausible explanation or a passing test suite can make a patch easier to understand, but neither shows that it meets the requirement or handles security boundaries correctly. GitHub recommends reviewing generated code in context and using tests and static analysis as part of the process (GitHub’s guide to reviewing AI-generated code).
A repeatable review routine
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Restate the intended change
Read the issue, acceptance criteria, or design first. In your own words, identify the expected behavior and compare it with the patch. Check whether it solves the requested problem and follows project conventions. Flag code that appears unrelated, even if it looks polished.
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Read the complete diff, file by file
Inspect every added, modified, and deleted file—not just the main source file. Include tests, lockfiles, CI configuration, deployment settings, and agent instruction or rules files. Look for changes outside the task’s scope, especially edits that remove safeguards or alter how checks run. OWASP cautions against approving AI-assisted changes based only on a summary or overlooking routine-looking edits (OWASP Secure Coding with AI Cheat Sheet).
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Trace important data and permissions
For each changed path that handles meaningful data or actions, ask: What can enter here? Where does the data go? Who is allowed to do this? Pay particular attention to input validation, output handling, authentication, authorization, secrets, and security-sensitive configuration. A feature can work as intended in a happy-path test while still exposing data or allowing an unauthorized action. Manual review is especially useful for business logic and context-specific flaws (OWASP Secure Code Review Cheat Sheet).
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Verify dependencies independently
Do not assume a package suggested by a coding assistant exists or is suitable. Check that it is real, appropriate for the project’s ecosystem, compatible with the project’s license requirements, and not known to have a vulnerability. Use the project’s established dependency-audit process or a suitable scanner. OWASP warns that AI tools may suggest hallucinated or outdated dependencies (OWASP Secure Coding with AI Cheat Sheet).
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Review tests as code
Inspect added, changed, and deleted tests. Ask whether assertions were weakened, tests were removed, or mocks replaced checks of real behavior. A green test suite is useful evidence, but it cannot establish that the tests encode the right behavior or that the change is secure. Where it matters, add or request tests for invalid input and important edge cases.
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Run the project’s usual checks
Build or compile the change, run relevant tests, review warnings, and use the static-analysis and dependency checks already available to the project. Record what ran and what did not, so reviewers know what evidence the change has. GitHub recommends tests and static analysis; OWASP recommends using tools alongside human review, not in its place (GitHub’s guide; OWASP Secure Code Review Cheat Sheet).
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Consider what the agent read and what it could access
If the agent processed issue text, comments, documentation, logs, or fetched web pages, treat that content as untrusted. Inspect the 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 Secure Coding with AI Cheat Sheet).
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Escalate high-stakes or unclear changes
Ask a reviewer with relevant expertise when a patch changes authentication, authorization, cryptography, sensitive-data handling, or deployment configuration—or when you cannot confidently explain what it does. A second review is also appropriate when the possible consequences are serious. The person accepting and committing the change remains accountable for it.
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What tests and scanners can—and cannot—tell you
Automated checks can run consistently across a codebase and help find known vulnerability patterns, dependency issues, build failures, and regressions covered by tests. They are valuable for scale and repeatability. But a tool cannot necessarily tell whether a feature’s business rules are correct, whether a permission check belongs in a particular path, or whether a test captured the requirement. Human review supplies that project and product context; it can also miss defects. OWASP describes secure code review as complementary to automated analysis such as SAST and DAST, rather than a substitute for it (OWASP Secure Code Review Cheat Sheet).
So, can you trust AI-generated code if all the tests pass? Not on that fact alone. Passing tests show that the checks you ran passed; they do not show that the tests are complete, that the diff is in scope, or that the code handles security correctly. Review the patch, the tests, and the relevant data and permissions before accepting it.
When to ask for help
Escalate rather than guess if you cannot follow a security-sensitive change, cannot tell whether a permission boundary is enforced, or see changes to secrets, authentication, cryptography, sensitive data, or deployment controls. You do not need to identify a specific exploit before asking for review: uncertainty about a high-impact change is enough. OWASP’s guidance states, “You are responsible for all code that you commit” (OWASP Top 10:2025, Next Steps).
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