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Review AI-generated code as if it came from an unknown contributor: reconstruct the intended change, read the complete diff, verify security and business behavior, inspect dependencies and tests, run the same automated gates used for human code, and obtain accountable human approval. Do not approve from an AI summary or a green test run alone. The approving developer remains responsible for correctness, security, and maintainability.
What standard should you apply to AI-generated code?
Use your repository’s normal review standard, then add explicit checks for omissions that language models commonly make. OWASP guidance states that every AI-assisted change should be reviewed, approved, and attributable to a developer responsible for its security and maintainability; no AI-generated code is exempt from code review, security scanning, or testing.
Think of the assistant as an untrusted contributor. It may produce plausible code while misunderstanding authorization, data sensitivity, failure modes, concurrency, or the project’s conventions. Your job is not to prove that the model wrote the code correctly. Your job is to establish that the submitted change satisfies the requirement and is safe in its actual operating context.
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- Read every changed file, not only the lines highlighted by the pull-request summary.
- Confirm behavior against an explicit requirement, acceptance criteria, or threat model.
- Check trust boundaries, sensitive data flows, and security controls independently.
- Verify every new dependency before it is installed or merged.
- Treat tests as evidence to inspect, not as proof that the implementation is correct.
- Run deterministic quality and security gates and investigate every warning.
- Require a named developer with suitable ownership to approve the change.
How do I reconstruct the intent and scope of the change?
Start before reading individual functions. Establish what the change is supposed to do, where it is allowed to act, and which components could be affected. A precise scope prevents an apparently tidy implementation from silently expanding permissions or altering unrelated behavior.
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Write the expected behavior in plain language
- Read the issue, design note, or acceptance criteria and state the user-visible outcome in one or two sentences.
- List inputs, outputs, side effects, failure behavior, and compatibility requirements.
- Identify data classifications involved, such as credentials, personal information, payment data, or internal tokens.
- Note performance, availability, and migration constraints that the code must preserve.
Map the complete change set
Review the pull request’s file list and compare it with the task. Include application code, tests, lockfiles, generated files, database migrations, configuration, build scripts, documentation that changes operational behavior, and any AI instruction files. Files such as AGENTS.md or .cursorrules can influence an agent’s actions and may contain unsafe or unapproved instructions.
Ask why each file changed. An unrelated formatting edit, a deleted fixture, or a lockfile entry can conceal risk even when the main function looks correct. If the scope cannot be explained, pause the review and ask the author to split or clarify the change.
Perform impact and integration analysis
Trace callers, data stores, queues, external services, and deployment paths touched by the change. Check backward compatibility for public APIs and message formats. Confirm that error handling, retries, timeouts, idempotency, and observability match the surrounding system rather than an isolated example generated by the assistant.
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What security omissions should I look for in the diff?
Read the code along every trust boundary. AI output often appears complete while omitting authorization checks, input validation, safe output handling, or secure failure behavior.
Authentication and authorization
- Verify that the code authenticates the caller before accessing protected resources.
- Check object-level authorization for every identifier supplied by a user; authenticating a session does not authorize access to another user’s record.
- Confirm that administrative actions require the intended role, scope, tenant, or capability.
- Inspect fallback paths, background jobs, webhooks, and internal endpoints, where authorization is easy to overlook.
- Ensure denial is the default when identity, policy, or permission data is missing.
Validation, encoding, and data flow
- Validate type, length, range, format, and allowed values at the boundary, then re-check assumptions at privilege changes.
- Use parameterized database queries and safe APIs instead of concatenating strings into SQL, shell commands, templates, or paths.
- Encode output for its actual sink: HTML, JavaScript, a URL, a log, a CSV file, or a command interpreter each has different rules.
- Trace secrets and personal data through logs, exceptions, telemetry, caches, and third-party calls.
- Confirm file uploads constrain size, type, names, storage location, and extraction behavior.
Cryptography and error handling
Reject invented or obsolete cryptographic constructions. Check algorithm choice, key generation, storage, rotation, nonce or IV handling, and verification order against your approved standards. Errors should fail closed without revealing secrets, stack traces, tokens, or whether a protected record exists. A generic error message must not prevent operators from receiving useful, access-controlled diagnostics.
Look for unfamiliar packages and hidden behavior
AI assistants can suggest packages that are misspelled, abandoned, typosquatted, or unrelated to the intended registry. A dependency that imports successfully is not automatically trustworthy. Inspect its provenance and the code path it adds before accepting the implementation.
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How should I verify dependencies introduced by an AI assistant?
Independently verify every package, action, image, module, and service reference before installation. Never add a dependency solely because an assistant named it.
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- Check release activity, maintenance status, licensing, transitive dependencies, and provenance against team policy.
- Prefer the project’s established, approved libraries when they provide the required capability.
- Pin or constrain versions according to repository policy and review the resulting lockfile diff.
- Run the ecosystem’s advisory checks, such as
npm audit,pip-audit,govulncheck, orcargo audit, together with your organization’s software-composition analysis. - Check current advisories in the National Vulnerability Database, the GitHub Advisory Database, or OSV when those sources are part of your process.
- Remove speculative packages and unused imports rather than accepting them “just in case.”
Dependency verification is both a security and a supply-chain control. A clean advisory report does not establish that a package is maintained, that its publisher is legitimate, or that your code uses it safely.
Can I trust the tests generated by an AI coding assistant?
No. Generated tests are useful starting points, but they are not independent assurance because they may encode the same misunderstanding or defect as the implementation. Passing tests show that the tested examples pass; they do not prove authorization, business rules, or resilience.
Inspect what changed in the test suite
- Look for deleted tests, narrowed fixtures, weaker assertions, and skipped or conditionally disabled cases.
- Check whether new mocks replace real integrations, authentication middleware, persistence, or authorization policy.
- Reject tests that merely assert the generated implementation’s exact output without checking the requirement independently.
- Ensure failures are meaningful: an assertion on a non-empty value is rarely enough for a security-sensitive result.
Add independent negative and adversarial cases
Write cases from the threat model and acceptance criteria, not from the implementation. Include invalid types and malformed payloads, missing and expired credentials, cross-tenant identifiers, boundary values, duplicate requests, replayed messages, partial outages, timeouts, concurrent updates, and unexpected ordering. For parsers and file handlers, include oversized, truncated, nested, and ambiguous input.
Run integration or end-to-end checks where mocks could hide wiring errors. Confirm that the test environment exercises the same configuration, feature flags, migrations, and permission model used in production. Security tests must verify both the allowed and denied paths.
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Run the repository’s deterministic gates locally when practical and require them in the pull-request pipeline. A useful gate set combines code quality, security, and build evidence.
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| Gate | What it can establish | What it cannot establish |
|---|---|---|
| Formatting and linting | Conformance to syntax and project style; some predictable defects | Correct business behavior or authorization |
| Type checking and compilation | Static type and build consistency | Runtime data validity, policy decisions, or safe integrations |
| Unit and integration tests | Behavior covered by the cases and environment | Unexamined paths, realistic adversarial behavior, or complete requirements |
| SAST | Patterns associated with common implementation weaknesses | Most business-logic and context-dependent access-control errors |
| Software-composition analysis | Known issues and policy violations in dependencies | Whether a dependency is necessary or safe in your specific data flow |
| Secret scanning | Known credential and token patterns in the change and repository | Whether a secret was transformed, logged, or exposed in a nonstandard form |
| Build, migration, and deployment checks | Packaging, schema, and release compatibility | Production traffic behavior and operational readiness without representative testing |
Investigate failures instead of rerunning until they disappear. Record the tool, result, and any approved exception in the pull request. A clean scan is a starting point: automated tools rarely understand the full business meaning of a permission check or data-flow decision.
What should I inspect in CI/CD, containers, and agent instruction files?
Infrastructure and automation are executable code and can change the repository’s trust boundary. Review them with the same care as application code, even when the ticket describes a small feature.
- Check workflow triggers, branch filters, permissions, reusable workflows, and whether untrusted pull-request code receives secrets.
- Review action and plugin versions; flag unpinned references and unexpected downloads or network calls.
- Inspect shell commands for injection, unsafe quoting, path assumptions, and commands that execute generated text.
- Examine Dockerfiles and build scripts for excessive privileges, untrusted base images, leaked build arguments, and writable sensitive paths.
- Confirm deployment files do not broaden network exposure, service accounts, filesystem access, or cloud roles.
- Read changes to
AGENTS.md,.cursorrules, Makefiles, package scripts, and similar instruction files for prompt-injection language or requests to disclose secrets, disable checks, or contact external systems. - Verify that logs and artifacts do not publish environment variables, credentials, source archives, or customer data.
If an agent was permitted to execute commands or access repositories, review its permission boundary and the commands it actually ran. The fact that an instruction came from a file in the repository does not make it trustworthy.
How do I decide whether the change needs specialist review?
Classify risk by the assets and boundaries affected, not by the number of changed lines. A small configuration edit can be more dangerous than a large isolated refactor.
| Change profile | Minimum workflow | Additional control |
|---|---|---|
| Low-risk, isolated feature with no sensitive data or privilege change | Normal peer review, full automated gates, and complete diff inspection | Independent negative tests for new input paths |
| Authentication, authorization, session, or identity logic | Named owner review, security-focused tests, and full gate set | Security champion or specialist review before approval |
| Cryptography, secrets, payment, personal data, or trust-boundary code | Qualified domain review plus SAST, composition, secret, and integration checks | Threat-model update and explicit approval of failure behavior |
| CI/CD, infrastructure, deployment, or agent execution | Infrastructure owner review and least-privilege inspection | Protected-branch and workflow-permission verification |
Use CODEOWNERS and protected branches to enforce ownership for sensitive paths. OWASP’s governance guidance is explicit that AI-generated code receives no exemption from review, scanning, or testing; high-risk changes should not be merged merely because a general reviewer is available.
What must the approving developer record?
Accountability should be visible in the normal development trail. Follow repository policy for disclosure, but make the origin and review decision traceable.
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- State in the commit or pull-request description that an AI assistant contributed, when policy requires that disclosure.
- Record the tool or model and the meaningful scope of its contribution if your organization requires those details.
- Describe the human validation performed: requirements checked, tests added, security gates run, and exceptions accepted.
- Ensure a named developer has read and understood the complete change rather than approving a generated summary.
- Link approvals, review comments, and any risk acceptance to the pull request so the decision remains auditable.
UK Home Office engineering guidance says teams retain full accountability for AI-assisted code and outputs. Attribution is therefore not a substitute for review; it is evidence that a responsible person can be identified after the change ships.
What does a practical pre-submission workflow look like?
Use this sequence when preparing an AI-assisted change. Stop and escalate whenever a step produces an unexplained result.
- Freeze the intended scope. Copy the requirement and acceptance criteria into your review notes. Identify affected components, data classes, and sensitive paths.
- Inspect the complete diff. Include tests, lockfiles, generated output, workflows, build files, migrations, and instruction files. Explain every changed file.
- Trace behavior. Follow inputs from the entry point through validation, authorization, business logic, storage, external calls, and output. Check error and retry paths.
- Challenge security assumptions. Test unauthenticated, unauthorized, malformed, oversized, replayed, concurrent, and cross-tenant cases as appropriate.
- Verify dependencies. Confirm registry, provenance, maintenance, policy compliance, version constraints, and advisories before installation.
- Audit the tests. Look for deleted coverage, weak assertions, over-mocking, and tests that simply mirror the implementation. Add independent cases.
- Run all gates. Execute formatting, types, builds, unit and integration tests, SAST, composition analysis, and secret scanning required by the repository.
- Review execution surfaces. Check workflows, containers, scripts, deployment permissions, network calls, and agent instructions for unsafe expansion.
- Route ownership. Request CODEOWNERS, security, infrastructure, or domain approval for sensitive paths.
- Record accountability. Document AI assistance and the human checks, then submit only when the named approver understands the change.
What are common failure modes and how do I recover?
The summary looks right, but the diff is larger than the task
Close the pull request or ask for a split. Rebase from a clean branch, regenerate only the required files, and review the new diff from the repository’s base commit.
A suggested package cannot be verified
Do not install it to investigate in the production project. Remove it, search the approved registry and internal catalog, and choose an established alternative or implement the small required behavior locally.
Tests pass after mocks replaced real services
Restore an integration path or add a contract test that exercises the real boundary in a controlled environment. Keep focused unit tests, but do not treat mock-only coverage as evidence that the integration works.
A scanner reports a finding in generated code
Trace whether the generated file ships, runs, or contains secrets. Fix the underlying source or generation step, regenerate, and rerun the scanner; suppress only with a documented, reviewed justification.
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An authorization check is difficult to explain
Stop approval. Ask the service or domain owner to state the policy in concrete terms, add allowed and denied tests, and route the change to the appropriate owner before reconsidering it.
The workflow needs a secret or broader permission than before
Rework the job around least privilege, isolate untrusted code, and use a protected environment or approved identity mechanism. Never solve uncertainty by granting repository-wide secrets or administrator permissions.
How can teams compare review workflows?
Choose a workflow by risk coverage and accountability, not by how quickly an assistant produces a patch.
| Evaluation axis | Lightweight feature workflow | High-risk workflow |
|---|---|---|
| Change-risk coverage | Scope and impact review for isolated components | Threat-model and trust-boundary analysis with domain ownership |
| Human review depth | One qualified peer reads the full diff | Named code owner plus security or infrastructure specialist where applicable |
| Automated coverage | Formatting, types, tests, and repository-required scans | All standard gates plus focused SAST, composition, secret, integration, and deployment checks |
| Dependency provenance | Registry, version, advisory, and policy checks | Those checks plus explicit approval for new suppliers or privileged components |
| Test independence | Review assertions and add negative cases | Adversarial, authorization, failure-injection, concurrency, and representative integration tests |
| Agent permissions | No unnecessary write, network, or secret access | Isolated execution, least privilege, protected secrets, and recorded actions |
| Auditability | AI disclosure and ordinary review trail | Tool/model record, named approvers, risk decision, and retained evidence |
| Reviewer effort | Comparable to a normal small change | Higher effort justified by asset value and blast radius |
Frequently Asked Questions
Should an AI-assisted pull request include the exact prompts used?
Only if your organization’s policy or an audit requirement calls for them. At minimum, preserve the tool or model identity, the scope of assistance, the final diff, review comments, approvals, and gate results; prompts can contain confidential data and should be retained under the same access controls as other engineering records.
What if the assistant edited code in several programming languages?
Assign reviewers who understand each affected runtime and its security model, and run each ecosystem’s formatter, type checker, dependency audit, and test commands. A single reviewer may approve the integration while missing language-specific issues such as unsafe deserialization or package-resolution behavior.
How should I handle generated documentation or comments in the same pull request?
Verify examples, command snippets, configuration names, and links by executing or checking them against the current system. Incorrect documentation can cause operators to disable controls or deploy an unsafe configuration even when the application code itself is sound.
Can a post-merge review compensate for skipping approval before submission?
Use post-merge review only as an emergency control with an explicit incident or risk-acceptance record. It does not replace required branch protection, ownership approval, or pre-merge security gates for sensitive changes.
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