Verify AI-generated code the same way you would any proposed change: read the full diff, test the stated requirements independently, review security and dependencies, and have a human owner approve what will be merged. Passing tests or an agent’s confident summary are useful signals—not proof that the change is correct, safe, or within scope. When an AI tool can run commands, access files, or use the network, review its permissions and actions as well as its code.
Start with the change you asked for
Before generating code, write down what the change must do and what it must not do. Without clear acceptance criteria, a reviewer cannot reliably tell whether a plausible-looking implementation meets the actual requirement.
- Expected behavior: Describe inputs, outputs, state changes, and user-visible results.
- Constraints: Name compatibility requirements, affected components, performance or privacy requirements, and files or systems that should not change.
- Security assumptions: For sensitive features, identify trust boundaries and who is allowed to perform each action.
- Evidence expected: Specify relevant tests, build checks, or other verification you expect before review.
These criteria become the standard against which you review both the code and the agent’s work. They also make it easier to notice when the implementation solves a nearby problem rather than the one you assigned.
Match your review to the AI workflow
“AI-generated code” can mean anything from a small completion accepted by a developer to an agent that edits several files and executes tools. The latter adds operational risks because the system may create side effects beyond the code it writes.
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| Workflow | What the tool can do | What to verify |
|---|---|---|
| Code completion or chat suggestion | Propose text or code for a developer to select and apply. | Review the accepted code in context, including its behavior, dependencies, and tests. The developer’s selection does not establish that the suggestion is correct. |
| Autonomous or agentic tool | Depending on its configuration, edit multiple files, run commands, install packages, access networks, or push changes. | Review the diff and also check the tool’s permissions, actions, logs where available, and any external resources or credentials it could access. |
An agent’s summary can help you navigate a change, but it is not a substitute for reading the changed files. OWASP’s AI Agent Security Cheat Sheet provides guidance on securing agents; the practical implication for code review is to treat their actions and operating environment as part of the review surface.
Review the full diff before running with the summary
Compare every changed file with the task’s acceptance criteria. Check not only the main implementation but also files that can alter how code is built, tested, deployed, or reviewed.
- Look for unrelated edits, broad formatting changes, generated files, or changes outside the requested area.
- Inspect tests for deleted cases, weaker assertions, or mocks that bypass the behavior the test is supposed to exercise.
- Pay particular attention to dependency lockfiles, CI configuration, build scripts, security rules, and agent instruction files.
- Where the tool allows it, restrict its writable scope to the files or directories needed for the task.
Investigate unexplained changes rather than assuming they are harmless cleanup. A modification to a workflow, build script, or instruction file can affect future automation even if the application code looks reasonable.
Use tests as evidence, not a verdict
Run the project’s relevant tests and build or type checks, but assess what those checks actually cover. A test suite can pass while missing a required behavior, an invalid input, or a security-relevant case. Be especially cautious when the same agent wrote both the code and the tests: it may encode the implementation’s assumptions in both.
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OWASP’s Secure Coding with AI Cheat Sheet states: “A passing test suite generated by the same agent that produced the code provides no independent assurance.” Treat that as review guidance, not an empirical claim about defect rates.
Choose additional cases from the requirement and its failure modes, not just from the implementation. Depending on the feature, consider:
- Invalid, missing, malformed, or unexpectedly large inputs.
- Boundary values, empty collections, and unusual state transitions.
- Unauthorized users, expired credentials, and requests for another user’s data.
- Concurrent requests or retries if they can create conflicting updates or duplicate actions.
- Error paths: whether failures are handled safely and whether the right information is exposed to users or logs.
OWASP’s AI Testing Guide frames testing as a multidisciplinary trustworthiness practice for autonomous and semi-autonomous systems. For an application change, the useful lesson is to select checks for the behavior and risks at hand rather than relying on a single passing result.
Use each verification method for what it can show
Different checks answer different questions. None establishes every dimension of correctness, security, and fit with the product’s intent.
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| Method | Useful evidence | What it does not settle on its own |
|---|---|---|
| Unit and integration tests | Whether selected behaviors work under the inputs and conditions the tests exercise. | Whether untested requirements, edge cases, or threats are handled correctly. |
| Static analysis | Whether the code matches patterns a configured analyzer recognizes, including some common defects or risky constructs. | Whether business rules and context-specific security requirements are correct. |
| Dependency analysis | Whether checked packages have known risks identified by the tool and its data sources. | Whether a package is the intended one, appropriate for the project, or free of every risk. |
| Dynamic testing | How the running system behaves under the particular scenarios exercised. | Behavior in scenarios that were not run, or correctness of the design and requirements. |
| Manual review | Whether a reviewer can connect implementation details to intent, business logic, and surrounding context. | A guarantee that every defect will be found; review quality depends on access, understanding, and scope. |
OWASP describes secure code review as manual examination for vulnerabilities that automated tools often miss, and emphasizes its complementary role alongside automated checks. See the Secure Code Review Cheat Sheet.
Trace security-sensitive behavior in context
For changes involving identity, permissions, user data, external input, or sensitive operations, follow the relevant data and control flows. Do not stop at a line that appears to validate input; check whether the validation happens before the sensitive operation and applies to every path that reaches it.
- Input validation: Check that untrusted values are constrained and handled consistently, including malformed and unexpected inputs.
- Authentication and authorization: Verify not only that a user is authenticated, but that the action and specific resource are authorized.
- Output handling: Check that data is encoded or otherwise handled appropriately for the context in which it is displayed or consumed.
- Cryptography: Examine choices and usage in the feature’s context rather than accepting an unfamiliar implementation without review.
- Errors and logs: Check what happens when dependencies or operations fail, and whether error messages or logs expose information they should not.
Automated scans can identify recognized patterns, but they cannot by themselves establish whether a business rule is enforced correctly for your application. Manual review is particularly important where permissions and product-specific logic meet.
Verify packages instead of trusting suggestions
An AI-generated import or installation command is not evidence that the package exists, is the intended project, or is safe to use. Before adding a dependency, verify its identity and suitability through your organization’s normal process. Check maintenance and provenance signals where that process calls for them, inspect the version, and run the usual dependency security checks for known vulnerabilities.
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OWASP’s secure-coding guidance warns against blindly installing package names suggested by AI and against assuming that suggested versions reflect current vulnerability information. Keep dependency additions visible in the diff and review any associated lockfile changes rather than treating them as routine noise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Treat content read by an agent as untrusted
An agent may read repository documentation, issues, pull request comments, web pages, dependency notes, logs, or tool responses. Some of that content may be attacker-controlled or simply irrelevant; text inside it should not automatically be treated as an instruction with authority over the task. This risk is commonly described as indirect prompt injection.
Limit the context an agent receives to what it needs, and check its actions when it consumes external or repository content. In particular, investigate unexpected commands, network requests, file changes, or attempts to access sensitive information. An instruction in a README or issue does not make an out-of-scope action safe.
Limit the agent’s access and inspect its side effects
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Changes to CI/CD workflows, build scripts, dependency configuration, and agent instruction files deserve focused attention because they can affect execution or future automated work. For guidance on agent threats and controls, consult OWASP’s AI Agent Security Cheat Sheet.
Make approval a human decision
The person approving a change should be able to explain what it does, why it meets the acceptance criteria, and what the tests establish. If the reviewer cannot understand the change or resolve important findings, it is not ready to merge. An AI-generated review, passing check, or agent summary can contribute evidence, but none transfers responsibility for approval.
OWASP’s AppSec Agent is an example of an open-source project describing AI-supported security review, pull-request analysis, threat modeling, fix generation, and test verification. Its existence is not an independent evaluation or endorsement, and using an additional AI tool does not replace an accountable human reviewer.
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