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Reviewers should block AI-generated changes they cannot explain, validate, or safely maintain. A pull request also stays blocked when it fails its requirements or tests, contains an unresolved critical security finding, or changes a high-risk security surface without the extra human or specialist review required by policy. AI provenance is a risk signal, not a defect verdict: generated code must meet the same secure-development standard as hand-written code, with additional attention to the agent’s permissions, the external material it read, and the files it changed.
What “block” means for an AI-generated pull request
Blocking is a release-control decision, not a judgment about whether a developer used an AI assistant. The reviewer declines approval until the change has adequate evidence, an accountable owner, and any required exception. If the evidence cannot be produced, the practical outcomes are to request changes, narrow the diff, add testing or specialist review, or reject the implementation and ask for a rewrite.
OWASP’s AI secure-coding guidance says every AI-assisted change needs a human owner who understands and approves it. OWASP’s AI-specific verification guidance adds separation of duties: the person who requested generation must not be the only reviewer, and the AI agent itself is not a human reviewer. That requirement applies whether the model wrote one function or an entire service.
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The seven conditions that should stop approval
1. The behavior is unexplained or unverifiable
Block when the author or reviewer cannot state what a nontrivial block does, why it is necessary, what assumptions it makes, and how it satisfies the issue or specification. “The model generated it” is not an explanation, and a confident automated comment is not evidence. Ask for a smaller diff, a design note, executable tests, or a rewrite that the named owner can maintain.
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2. A critical security finding remains open
OWASP’s AI Verification Standard gives a pull-request blocking example of CVSS 9.0 or higher, or the equivalent threshold defined by an organization’s policy. Do not merge while such a finding is unresolved unless an authorized human exception is documented. The exception should identify the risk owner, rationale, compensating controls, expiry or review date, and the exact finding being accepted.
3. A security-critical surface missed elevated review
Use the organization’s two-person or security-team route when generated code changes authentication, authorization, cryptography, identity and access management (IAM) policy, CI/CD workflows, deployment manifests, sandbox policy, or network policy. A normal peer review is not a substitute when policy requires an additional reviewer or specialist sign-off.
4. Security behavior is inadequately tested
Block input validation, authorization, deserialization, and similar paths when tests do not exercise realistic boundary conditions and failure behavior. OWASP AISVS specifically points to differential fuzzing or property-based tests for these areas. Generated unit tests count as test artifacts, not as proof that the behavior is safe; inspect what they assert and what they omit.
5. Dependencies, versions, or secrets are unverified
Reject a package addition when its purpose, source, version, license or permissions are unclear, or when the version has not been checked against current vulnerability data. Also block credentials, tokens, private keys, or sensitive configuration that were introduced, logged, committed, or copied into tests. A dependency scanner can identify known advisories, but it cannot decide whether the package is justified or whether its runtime privileges are excessive.
6. The agent made unexpected or unrelated changes
Stop when the diff exceeds the requested scope, removes checks, weakens controls, adds network access, changes permissions, or edits unrelated files after the agent processed issues, pull-request comments, README files, logs, changelogs, web pages, or dependency documentation. Those inputs can contain indirect prompt-injection instructions. Treat the resulting diff as untrusted until every unexpected change is explained and either removed or explicitly approved.
7. AI review is presented as the only review
An AI review assistant can suggest issues for investigation, but its confidence score, approval comment, or “no findings” result does not satisfy accountable human review. The human reviewer owns validation, approval, and the decision to merge.
A practical review workflow
The following sequence keeps provenance, behavior, security, and evidence in one decision trail. Teams can adapt the order to their secure-development policy, but should not silently omit a control because code was generated.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute- Establish ownership and intent. Ask the author to describe the intended behavior, assumptions, affected interfaces, and acceptance criteria. Identify the human who will own correctness, security, and maintenance after merge. If that person cannot explain a generated block, return it for simplification or replacement.
- Read the whole diff and its context. Review additions, deletions, renamed files, generated artifacts, configuration, permissions, and test changes. Compare the result with the issue or specification and look for scope creep, removed assertions, altered trust boundaries, or newly reachable network paths.
- Record what the agent consumed. Determine whether the agent read repository instructions, issues, comments, logs, documentation, web content, or dependency metadata. Treat those materials as untrusted input. Investigate any output that appears to follow an instruction unrelated to the requested task.
- Verify requirements and behavior. Run the required unit, integration, regression, and end-to-end tests. Check failure handling, boundary values, concurrency assumptions, authorization decisions, and compatibility with existing callers. A passing test suite is necessary evidence, not a guarantee of correctness.
- Apply risk-based security review. Inspect parsing and validation, authentication and authorization boundaries, cryptographic use, sensitive-data handling, error paths, dependencies, secrets, CI/CD, infrastructure-as-code, deployment, sandbox, and network policy. Route high-risk surfaces to the additional reviewers required by policy.
- Run automated security checks. Use the repository’s required static analysis, interactive or dynamic testing, secret scanning, infrastructure-as-code checks, software-composition analysis, and dependency audits. Triage every result; do not treat a clean dashboard as approval.
- Perform targeted testing for security properties. For validation, authorization, and deserialization, add differential fuzzing, property-based tests, or adversarial cases appropriate to the threat model. Confirm that tests fail when the security property is deliberately broken.
- Decide, document, and retain evidence. Approve only when requirements, tests, findings, ownership, and required reviewers are satisfied. Record reviewers, checks run, findings and dispositions, dependency decisions, and any written exception. Block until unresolved critical issues or missing evidence are closed.
Where human review is strongest—and where tools are not enough
| Approach | What it can establish | What the reviewer must still check |
|---|---|---|
| Human code review | Requirements, business logic, authorization intent, complex security design, and context-specific behavior | The reviewer understands the change, has enough context, and has focused attention on the highest-risk paths |
| Static analysis and secret scanning | Detectable classes of weaknesses and exposed credentials | Findings are triaged; a clean result does not prove business rules or authorization are correct |
| Dependency analysis | Known vulnerabilities and malware advisories for packages and versions | The package, version, source, license, permissions, and update plan are justified |
| Unit and integration tests | Expected behavior, regressions, and interface compatibility | Cases reflect real requirements, including failures, boundaries, and security-relevant paths |
| Fuzzing and property-based tests | Input robustness and invariant violations across many generated cases | Properties model the actual security requirement and cover the correct trust boundary |
| AI review assistant | Additional leads that may reveal defects for investigation | Every lead is validated by a qualified human; the assistant does not approve or certify safety |
OWASP’s secure-code-review guidance notes that manual review is particularly valuable for business logic, complex security implementations, and vulnerabilities that depend on application context. NIST SP 800-218A, a July 2024 community profile for generative-AI and dual-use foundation-model development, advises organizations to define when code review or code analysis is required and to perform it under their secure-coding standards. Neither source establishes one universal tool or sequence for every team.
How to inspect the highest-risk code paths
Input handling and deserialization
- Identify every external source: HTTP bodies, headers, files, messages, environment variables, command-line arguments, and stored records.
- Confirm validation occurs at the trust boundary, uses an allowlist where practical, enforces size and type limits, and rejects malformed data safely.
- Check parser and deserializer settings for unsafe polymorphism, executable types, object hooks, and resource-exhaustion behavior.
- Test malformed, oversized, duplicate, truncated, and unexpected-but-well-typed inputs.
Authentication and authorization
- Trace identity establishment, session or token validation, expiry, audience, issuer, and revocation behavior.
- Verify authorization at the server-side operation and object level, not only in a user interface or route guard.
- Test deny-by-default behavior, tenant or resource isolation, privilege changes, and direct requests that skip normal navigation.
- Require the elevated review path for changes to authorization logic, IAM policy, roles, or permission boundaries.
Cryptography and sensitive data
- Check that approved libraries and algorithms are used with correct key sizes, modes, randomness, nonce handling, and certificate validation.
- Look for secrets in source, logs, error messages, fixtures, generated documentation, and build output.
- Verify encryption boundaries, key storage, rotation assumptions, and redaction of personal or confidential data.
- Do not accept a new cryptographic construction merely because the code is short or passes functional tests.
CI/CD, deployment, sandbox, and network policy
- Inspect workflow triggers, token scopes, artifact provenance, script execution, and branch protections.
- Review deployment manifests for exposed services, privileged containers, host mounts, unsafe defaults, and unintended environment access.
- Check sandbox and network rules for newly permitted egress, ingress, filesystem access, or inter-service trust.
- Require the policy-defined specialist or two-person review before merging changes on these surfaces.
Dependencies and the agent’s context are part of the attack surface
AI assistants often propose packages to solve a narrow task. Before accepting one, establish why the dependency is needed, whether an existing approved library already provides the capability, which exact version and source are intended, what permissions it receives, and how it will be updated. Check the selected version against vulnerability databases such as the National Vulnerability Database, the GitHub Advisory Database, or OSV, while recognizing that advisories do not replace a suitability review.
Agent context deserves the same scrutiny as code. Repository instructions, issue text, comments, logs, documentation, and copied web content can contain instructions designed to redirect an agent. A safe review asks what the agent was allowed to read and write, whether it had network access, and why each resulting permission or file change exists. Unexpected behavior is a reason to pause and inspect the full session or audit trail, not to trust the final diff because it compiles.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available evidence says about AI-code risk
A 2025 arXiv preprint analyzed public GitHub repository files attributed to four AI tools with CodeQL. It reported 4,241 CWE instances across 77 vulnerability types in 7,703 files, while 87.9% of the analyzed files had no identifiable CWE-mapped vulnerability. Those figures describe a selected corpus and one static-analysis method; they do not estimate the probability that an individual pull request is unsafe.
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The same study reported language-specific observed rates of 16.18%–18.50% for Python, 8.66%–8.99% for JavaScript, and 2.50%–7.14% for TypeScript. These ranges are study-specific and should not be treated as universal language rankings or as a reason to waive review in one language.
Best Value
A 2024 qualitative study of AI assistants in software development found participants describing manual inspection, peer review, and testing that included unit tests, static analysis, and fuzzing. One participant, identified as P17, said: “Code that’s generated by the AI goes to the exact same review process [. . .]” That interview statement describes one participant’s practice; it is not a measured result for all teams.
The evidence supports a consistent control: review AI-generated code as software, apply risk-based security checks, and keep an accountable human in the approval chain. It does not support declaring generated code inherently insecure or inherently safe.
AI-generated code versus AI-reviewed code
AI-generated code concerns provenance: a model produced or materially edited the implementation. The reviewer should ask what instructions and context shaped it, whether the output expanded scope, and whether the owner can maintain it.
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How to document a defensible approval or block
- Intent: link the change to its requirement, threat model, or issue and state the expected behavior.
- Ownership: name the human responsible for correctness, security, and maintenance, plus the independent reviewer where separation of duties applies.
- Scope: record files, permissions, trust boundaries, dependencies, and agent-access implications that were examined.
- Evidence: list tests, fuzzing or property checks, static and dynamic scans, secret scans, infrastructure checks, and dependency audits.
- Findings: record each security or quality issue, its disposition, and why any residual risk is acceptable.
- Exceptions: attach the written, authorized exception for any critical finding that policy permits to bypass, including its owner and expiry.
If any of these records cannot be completed for a high-risk change, keep the pull request blocked until the missing evidence or review is supplied.
A reviewer decision matrix
| Observed condition | Decision | Required next action |
|---|---|---|
| Requirements are met, tests pass, behavior is understood, and required reviewers approve | Approve | Record the checks and accountable owner |
| Minor defect or unclear code with no immediate security impact | Request changes | Specify the correction and evidence needed before re-review |
| Critical automated finding, CVSS 9.0 or higher example threshold, with no authorized exception | Block | Remediate, retest, and rerun affected scans |
| Authentication, authorization, cryptography, IAM, CI/CD, deployment, sandbox, or network-policy change without elevated review | Block | Route to the required second reviewer or security specialist |
| Agent changed unrelated files, weakened controls, or added unexplained access | Block | Reconstruct context, remove unauthorized changes, and reassess permissions |
| Owner cannot explain or maintain a nontrivial generated section | Block | Reduce scope, rewrite, or assign an accountable owner who can validate it |
Bottom line for reviewers
Do not block a pull request merely because a model wrote it, and do not approve it merely because it compiles or an AI reviewer found no issue. Block what cannot be explained, tested, secured, maintained, or independently reviewed. Unresolved critical findings, missing evidence on security-sensitive behavior, unverified dependencies or secrets, unexpected agent-driven changes, and absent human accountability are all sufficient reasons to stop the merge.
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