Reviewers should block AI-generated code when they cannot explain or validate its behavior, when it fails requirements or tests, when critical security findings remain unresolved, or when a high-risk change lacks the required human and specialist review. AI provenance is a reason to examine the change carefully, not proof that it is defective—and it never lowers the standard applied to ordinary code.
The decision rule: block evidence gaps, not the mere use of AI
An AI-generated pull request is acceptable only when a qualified human can take responsibility for its correctness, security and maintenance. The reviewer should be able to describe what the change does, why it is needed, which assumptions it makes, and how the evidence supports approval. If that understanding is missing, the correct decision is to block or request a smaller rewrite.
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The same rule applies whether code was written by an autonomous agent, produced from a chat prompt, or completed by an editor assistant. Generated tests, an AI review bot’s confidence score, and a clean static-analysis report are evidence to investigate; none is approval by itself.
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| Block condition | What the reviewer should require before approval |
|---|---|
| Unexplained or unverifiable behavior | A clear description of intent, assumptions, affected paths and maintenance implications from an accountable human owner. |
| Requirements or tests are not satisfied | Passing, relevant tests and evidence that required behavior, failure handling and boundary cases are covered. |
| Critical security finding | Remediation, or a written exception approved by the person and process named in security policy. OWASP AISVS uses CVSS 9.0 or higher as an example critical threshold. |
| Security-critical change without elevated review | The additional reviewer, two-person approval or security-team sign-off required by the organization’s policy. |
| Unverified dependency, package source or version | Evidence that the dependency is necessary, authentic, supported and free of known unacceptable vulnerabilities or malware advisories. |
| Secrets or permissions exposed | Removal or rotation of credentials, least-privilege configuration and confirmation that logs, artifacts and history are clean. |
| Unexpected scope or trust-boundary changes | An explanation for unrelated file edits, new network access, weakened controls or changes caused by untrusted instructions in issues, comments, documentation or logs. |
These are stop conditions, not a claim that every AI-written line is unsafe. A low-risk formatting change can need little scrutiny; a five-line authorization change can require a security specialist. Review depth should follow impact and threat model.
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Accountability comes before approval
A qualified human must own the change
OWASP’s AI security verification guidance requires AI-generated code to receive review by a qualified human engineer who is not the same identity that requested generation. The AI agent does not count as that reviewer. OWASP’s Secure Coding with AI guidance makes the accountability requirement explicit: every AI-assisted change needs a human owner responsible for security and maintainability.
In practice, ask the author or proposed approver to explain the intended behavior, important assumptions, data flows and operational consequences. For a nontrivial generated block, inability to explain why it exists is itself a review failure. Request a narrower change or a human rewrite instead of approving code that the team cannot safely modify later.
AI-generated and AI-reviewed are different claims
Generated code has a provenance question: how was the implementation produced and what context did the agent receive? AI-reviewed code has an analysis question: what did an automated reviewer flag or miss? An AI review assistant can surface leads, but it cannot independently certify a pull request. The human reviewer still has to reproduce, test and disposition each material finding.
Separate duties when risk warrants it
For ordinary changes, a qualified peer may be sufficient. For sensitive paths, require the additional approver named in policy. Separation of duties is particularly important when the same person prompted an agent, accepted its output and controls the deployment decision.
A practical review workflow
1. Establish intent and ownership
- Read the issue, specification or change request before reading the generated implementation.
- Ask the owner to state the intended behavior, inputs, outputs, failure modes and assumptions in plain language.
- Confirm who is accountable for correctness, security and future maintenance.
- Compare the stated intent with the actual diff. Block if the owner cannot explain a nontrivial section or if the implementation solves a different problem.
Generated tests should be treated as part of the proposal, not as independent evidence. A test that merely mirrors the implementation can pass while the implementation violates the requirement.
2. Review the complete diff and its context
Inspect every changed file, not only the files the agent says are relevant. Look for removed tests, disabled checks, altered build scripts, new environment variables, permission changes, logging of sensitive data and modifications outside the requested scope.
Record what the agent read. Issues, pull-request comments, README files, logs, changelogs, web pages and dependency documentation can contain instructions aimed at the agent rather than the software. OWASP describes these as untrusted inputs because indirect prompt injection can cause an agent to weaken controls, add unrelated edits or disclose data. A surprising file change after the agent processed external text is a reason to stop and investigate.
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3. Verify requirements and behavior
- Map each acceptance criterion to code and to at least one meaningful check.
- Run the repository’s required unit, integration, end-to-end and regression tests.
- Check failure behavior, timeouts, retries, concurrency, malformed input and partial outages.
- Compare behavior with the previous version where compatibility matters.
- Block if tests fail, essential cases are absent, or the reviewer cannot predict the result for realistic inputs.
For input validation, authorization and deserialization, OWASP AISVS specifically points to differential fuzzing or property-based testing. These techniques can expose behavior that a few hand-written examples miss. The test oracle still needs to express the security property; generating more examples without a trustworthy oracle does not prove safety.
4. Inspect security-sensitive surfaces
Trace data from entry points to storage, external services and responses. Pay particular attention to:
- Input parsing, canonicalization, validation and output encoding.
- Authentication, session handling, authorization decisions and tenant isolation.
- Cryptographic algorithms, key storage, random-number generation and certificate validation.
- Deserialization, template rendering, command execution and file-system access.
- Error messages, logs, telemetry and handling of personal or secret data.
- Dependency manifests, lockfiles, build scripts and generated artifacts.
- Infrastructure-as-code, deployment manifests, CI/CD workflows, sandbox settings and network policy.
Authentication, authorization, cryptography, IAM policy, CI/CD workflows, deployment manifests, sandbox policy and network policy are elevated-risk areas under OWASP AISVS. Route such changes through the required two-person or security-team review rather than treating them as routine application edits.
5. Run required automated checks
Use the controls required by the repository and its secure-development policy. OWASP AISVS lists static application security testing, interactive and dynamic analysis where applicable, secret scanning, infrastructure-as-code scanning and software-composition analysis. OWASP also recommends dependency-audit tools and checking versions against vulnerability sources such as the National Vulnerability Database, the GitHub Advisory Database and OSV.
Interpret results correctly. A finding normally requires triage: confirm reachability, exploitability, affected versions and compensating controls. Conversely, a clean report only means that the selected tools found nothing in their detectable categories. It does not establish that a business rule is correct, that an authorization check is placed at the right boundary or that a context-specific vulnerability is absent.
6. Decide, document and handle exceptions
Block when a critical finding remains unresolved, required evidence is missing, the change is not understood, a security-critical path lacks elevated review, or the named owner cannot maintain it. Record the reviewer identities, checks performed, material findings, remediation and final disposition.
Do not use an informal comment such as “the model probably meant well” as an exception. For a critical scanner result, use only the written exception route authorized by policy, with a named approver, rationale, compensating controls and an expiration or review date where your process requires one.
How to review the highest-risk code paths
Authentication and authorization
Confirm that every protected operation enforces authentication and the correct authorization decision at the server-side boundary. Check object-level access, role and tenant checks, default-deny behavior, token audience and expiry, session invalidation and privilege changes. A test that verifies a permitted request is not enough; include denied, cross-tenant and expired-credential cases.
Block if authorization is inferred from client-controlled fields, if a check exists only in a user interface, if a route bypasses the shared policy middleware, or if the generated code changes role semantics without an explicit requirement and specialist review.
Input validation and deserialization
Identify the parser, the canonical representation and the validation boundary. Ensure limits exist for size, depth, recursion, number of elements and processing time where untrusted input is accepted. Check that validation happens after the same decoding and normalization used by downstream code; validating one representation and using another can create bypasses.
For deserialization, verify that permitted types and classes are allow-listed, dangerous callbacks are disabled and untrusted data cannot invoke arbitrary code or access internal objects. Use property-based or differential fuzzing when the path is security-sensitive, and block if the test corpus covers only well-formed happy paths.
Cryptography and secrets
Check algorithm choices, key lengths, nonce or IV handling, random-number sources, certificate validation, key rotation and failure behavior. Generated code often adds a library call that looks plausible but uses a weak mode, a hard-coded key or an insecure fallback. Confirm that secrets are obtained through the approved secret-management path and are absent from source, logs, test fixtures, build output and pull-request metadata.
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CI/CD, deployment and policy files
Review workflow triggers, token scopes, third-party actions, artifact provenance, deployment identities, network egress, container privileges and environment separation. Compare the generated manifest with the platform’s effective configuration, not just its syntax. A syntactically valid policy can still grant broad access.
Any change that expands agent permissions, adds network access, disables a gate or changes sandbox boundaries should be considered a trust-boundary change. Require the elevated review path even when the diff is small.
Dependencies and agent context need their own checks
Audit every suggested package
For each new or changed dependency, establish why it is necessary, who publishes it, which exact version and source are intended, what permissions it receives, and whether it is maintained for the target platform. Check vulnerability and malware advisories and verify that the lockfile resolves the reviewed artifact. Do not accept a package merely because its name appears in generated code or because a test passes with it installed.
Review transitive changes as well. A seemingly small package can pull in a large dependency tree, alter licensing obligations or introduce install-time scripts. If the source, version or permissions cannot be justified, block until the owner supplies evidence or removes the dependency.
Treat repository and web content as untrusted instructions
An agent may ingest issue text, comments, documentation, logs or dependency pages while deciding what to change. Those materials can include indirect prompt-injection instructions. Review whether the resulting diff contains unrelated files, disabled checks, exfiltration attempts, new network calls or instructions embedded in comments that are executed by build tooling.
Limit agent credentials and network access to what the task needs. Require the agent’s proposed commands, file list and permission changes to be visible to a human when the platform supports that control. The final review must still examine the output; access controls reduce blast radius but do not validate the code.
What each review technique can and cannot establish
| Technique | Useful evidence | Blind spot that still needs human review |
|---|---|---|
| Human code review | Requirements, business logic, authorization intent, complex security design and context-specific behavior. | It depends on reviewer understanding and finite attention; scope and risk must be prioritized. |
| Static analysis and secret scanning | Detectable classes of weaknesses, insecure patterns and exposed credentials. | Findings need triage, and no finding does not prove business logic or authorization correctness. |
| Dependency analysis | Known vulnerabilities, malicious packages and advisory matches for selected versions. | Whether the package choice, source, version and permissions are justified for this system. |
| Unit and integration tests | Expected behavior, regressions, boundary cases and compatibility. | Whether the tests represent real requirements and exercise security-relevant paths. |
| Fuzzing and property-based tests | Unexpected parser, validation and state-machine behavior under broad inputs. | The property or oracle may be incomplete; passing generated cases is not a complete specification. |
| AI review assistant | Additional leads and pattern-based questions for investigation. | It can miss flaws or repeat the implementation’s assumptions; a human owns validation and approval. |
NIST SP 800-218A, published in July 2024, describes secure-development practices for generative-AI and dual-use foundation-model development. It supports defining when code review or code analysis is required under an organization’s secure-coding standards; it does not impose one universal sequence or regulation for every team.
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How to interpret available evidence about AI-generated vulnerabilities
A 2025 arXiv preprint, Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories, reported 4,241 CWE instances across 77 vulnerability types in 7,703 files attributed to four AI tools. The same analysis reported that 87.9% of the files had no identifiable CWE-mapped vulnerabilities. Its language-specific observed rates were 16.18%–18.50% for Python, 8.66%–8.99% for JavaScript and 2.50%–7.14% for TypeScript.
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Those figures come from CodeQL findings in a selected public-repository corpus. They are not the probability that a particular pull request is unsafe, and they do not rank languages universally. File attribution, repository selection, tool mix and the weaknesses detectable by CodeQL all affect the result. Use the study to justify disciplined review, not to approve or reject an individual diff.
A 2024 qualitative study on security practices in software development reported participants describing manual inspection, peer review and testing that included unit tests, static analysis and fuzzing. One participant, identified as P17, said that AI-generated code went through the “exact same review process.” That is an interview statement about one participant’s practice, not a measured outcome for all organizations.
Examples of block decisions
Block: authorization helper changed without specialist review
An agent replaces a centralized permission check with a role comparison in a controller. Unit tests for an administrator pass, but no cross-tenant denial test exists and the change did not receive the required second reviewer. Block it. The missing negative cases and absent elevated review are independent failures; both must be resolved.
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Block: clean scan, unexplained network call
A generated utility passes static analysis and tests, but the diff adds an outbound request to a host not mentioned in the issue. The agent had read a repository comment containing setup instructions. Block it, isolate the change and determine whether untrusted text influenced the output. A clean scan cannot authorize an unexplained trust-boundary change.
Block: critical finding with no approved exception
A scanner reports a CVSS 9.4 vulnerability in a newly added package. The team cannot upgrade immediately and leaves the finding open with a review comment saying it is probably unreachable. Under the OWASP AISVS example threshold, this remains a merge block until remediated or covered by the organization’s written, authorized exception process with documented compensating controls.
Approve after evidence: isolated parser with appropriate tests
A generated parser is limited to a well-defined input format. The owner documents its assumptions, the diff contains no unrelated changes, dependency versions are audited, malformed and oversized inputs are tested, property-based tests cover parser invariants, required scanners are clean and an independent qualified reviewer signs off. The provenance is recorded, but the approval rests on understandable behavior and evidence.
A reviewer checklist for the final decision
- Ownership: Is a qualified human other than the requesting identity accountable for the change?
- Intent: Can that owner explain the behavior, assumptions, scope and maintenance plan?
- Diff: Did you inspect every file, removed check and permission change?
- Context: What issues, comments, logs, documentation or web content did the agent consume?
- Requirements: Does each acceptance criterion map to implementation and meaningful evidence?
- Security: Were validation, authorization, authentication, cryptography, secrets and error paths examined?
- Elevated review: Did authentication, IAM, CI/CD, deployment, sandbox or network-policy changes receive the required additional approval?
- Testing: Do tests cover realistic failures, denied cases, boundaries and security properties?
- Tooling: Were required SAST, DAST or IAST, secret, infrastructure and dependency checks run and triaged?
- Dependencies: Are package source, exact version, advisories, permissions and transitive changes justified?
- Findings: Are all critical issues fixed, or is there a written exception approved through policy?
- Record: Are reviewers, evidence, findings and disposition attributable in the change record?
The governing principle is simple: approve only what a responsible human can understand, test, secure and maintain. Block the rest, regardless of whether the first draft came from a person or an AI system.
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