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Review AI-generated code as a proposal, not as a finished solution: first establish what it is supposed to do, then inspect the full change, trace its data and permissions, challenge its behavior with independent tests, and run appropriate security checks. None of those steps guarantees that every bug will be found; together, they give a reviewer a disciplined way to look for defects that passing tests or automated scans may miss.
Start with the change’s purpose and risk
Before reading individual lines, read the issue, acceptance criteria, relevant architecture and security requirements. Identify which components, data, users, and assets the change can affect. For a pull request, be able to explain why each changed file is necessary and whether the change alters an existing security control. OWASP’s Secure Code Review Cheat Sheet recommends setting this context and prioritizing review effort.
Risk should shape the depth and independence of review. Changes involving authentication, authorization, cryptography, identity and access management (IAM) policies, CI/CD workflows, deployment manifests, sandboxing, or network policy deserve particular scrutiny. OWASP’s AI Security Verification Standard (AISVS), version 1.0, recommends a stricter threshold for security-critical code and configuration, such as two-person review or security-team sign-off.
Inspect the complete diff, including indirect changes
Read the entire change in context, not only the lines the assistant described. Look for scope expansion, unexpected files, changes to tests or security settings, and edits that weaken existing protections. Compare the result with the stated requirement: a plausible implementation can still do more, less, or something different from what was requested.
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When an AI agent can read repository content, issues, pull requests, logs, or tool output—and especially when it can run commands or edit files—treat that surrounding content as part of the review. OWASP’s Secure Coding with AI Cheat Sheet discusses prompt injection through repository context and tool integrations. Check persistent instruction files and unrelated edits rather than assuming the agent stayed within the prompt. For simple inline completion without those capabilities, this agent-specific risk is less central, but the diff still needs review.
Trace behavior, data, and authorization
Syntax review can show that code is well-formed; it cannot establish that it is correct for the product. Follow untrusted data from each entry point through validation, transformation, storage, and output. Check what happens when data is missing, malformed, oversized, stale, or deliberately crafted. Review error paths as carefully as the happy path, including whether errors expose sensitive information.
At every relevant boundary, confirm that authentication establishes who the caller is and authorization checks what that caller may do. A user-interface restriction is not a substitute for a server-side permission check. Follow tenant or account identifiers through queries and writes to make sure one user cannot access another user’s data.
Walk through business behavior beyond the typical case. Ask what happens with retries, duplicate requests, concurrent updates, partial failure, and boundary values. Check whether the result preserves the product’s actual invariants—for example, whether a payment is recorded once, a state transition is allowed, or a resource cannot be claimed by two users simultaneously. OWASP’s secure-review guidance identifies entry points, data flow, business logic, cryptography, errors, and configuration as review concerns; application-specific context is where a reviewer must apply judgment.
Examine security-sensitive implementation details
- Input and injection: Check validation, encoding, parameterized queries, command construction, and other places untrusted input reaches an interpreter or external system.
- Access control: Inspect permission checks on the server, object-level access, tenant boundaries, and privileged operations.
- Secrets and cryptography: Look for credentials in code or logs, unsafe key handling, and inappropriate cryptographic choices or usage.
- Parsing and errors: Review deserialization of untrusted data, failure behavior, and disclosures in responses or logs.
- Configuration and deployment: Check defaults, exposed services, permissions, network rules, and changes to build or release workflows.
For high-impact changes, do not let a clean scanner result substitute for a second qualified reviewer or security-team sign-off where your policy calls for it. AISVS gives CVSS ≥ 9.0 as an example threshold for a critical finding and recommends blocking merge unless an authorized human approves a written exception; that is a policy example, not a claim that lower scores are harmless.
Verify every proposed dependency
Do not accept a package name or version merely because generated code imports it. Confirm that the package exists, is the intended project, and comes from the expected source. Assess its provenance and maintainers, check the selected version against vulnerability information, and follow the team’s normal pinning and update process.
OWASP’s AI coding guidance warns that a suggested package name may be nonexistent and later registered by an attacker, or that a suggested version may be stale and carry known vulnerabilities. Software-composition analysis can help identify known dependency issues, but it does not establish that a package is trustworthy or appropriate for the use.
Review tests as claims, not proof
A green test suite establishes only that the tests that ran passed their assertions. Inspect test changes for deleted coverage, weakened assertions, mocks that bypass real behavior, and cases that simply encode the generated implementation rather than the requirement. A test can be internally consistent and still verify the wrong thing.
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- Invalid, malformed, missing, or boundary-value input.
- Expired or insufficiently privileged credentials and authorization failures.
- Duplicate requests, retries, concurrent operations, and partial failures.
- Unexpected state transitions and attempts to cross account or tenant boundaries.
For critical behavior, consider manually designed tests, property-based testing, or differential fuzzing where suitable. AISVS emphasizes verification practices for AI-assisted development; the key is to choose test scenarios independently of the generated code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use automated checks for the problems they can detect
Run the checks that fit the code and your delivery process: static application security testing (SAST), dynamic or interactive testing (DAST/IAST), secret scanning, infrastructure-as-code scanning, and software-composition analysis. Prefer checks that run consistently on pull requests, investigate findings, and apply a clear merge policy for critical issues.
| Review method | Useful for | What it cannot establish by itself |
|---|---|---|
| Human review | Requirements, business logic, contextual security controls, and whether the implementation fits the system. | It is not infallible; reviewers can overlook defects or misunderstand behavior. |
| SAST, DAST/IAST, secret, configuration, and dependency scans | Repeatable checks for issue classes the tools are designed to detect. | A clean result does not prove correctness, safety, or absence of context-specific vulnerabilities. |
| Tests | Specified behavior across the cases and assertions they actually exercise. | They cannot validate missing scenarios or incorrect expectations; passing tests may encode the wrong behavior. |
| AI review | Additional suggestions that may help direct a reviewer’s attention. | It does not provide accountable human approval or independent assurance merely because another AI inspected the code. |
Use findings to guide investigation, not as a substitute for understanding. OWASP notes that business-logic and context-specific vulnerabilities require human judgment. GitHub’s official Copilot responsible-use guidance states that inline suggestions may be syntactically correct without always being secure; that is vendor guidance about its product, not an independent claim about effectiveness or a guarantee that any tool catches a particular flaw.
Make approval accountable
A qualified human reviewer should understand the change and make the merge decision. AISVS calls for the reviewer to be a different identity from the person who prompted generation and does not count the AI agent as the reviewer. Keep approval attributable, and treat AI-generated review comments as advisory rather than sign-off.
OWASP’s guidance puts responsibility with the developer who accepts and commits generated code: “AI tools do not accept responsibility for the code they generate. The developer who accepts and commits the code does.” The practical implication is straightforward: if no reviewer can explain what the change does, why it is safe for its intended context, and what evidence supports that judgment, it is not ready to merge.
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