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Review AI-generated code as if it came from an unknown contributor: reconstruct what it is meant to do, read the entire diff, test the behavior independently, inspect security and dependency changes, and obtain accountable human approval before opening or merging the pull request. A green test run or clean scanner report is useful evidence, not proof that the code is correct or safe. The reviewer remains responsible for business logic, authorization, data handling, maintainability, and the changes made by the AI assistant.
The practical standard is the same one applied to human-written code, with extra attention to hallucinated packages, weakened tests, hidden instruction files, new network or shell behavior, and changes outside the requested scope.
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Apply your normal engineering and security gates without lowering them, then add checks for the distinctive ways AI assistance can create risk. OWASP’s guidance says every AI-assisted change should be reviewed, approved, and attributable to a developer responsible for its security and maintainability. The UK Home Office engineering guidance likewise states that teams retain full accountability for AI-assisted code and outputs.
That means the approving developer must be able to explain the change, identify its trust boundaries, describe how it was tested, and defend the dependencies and operational settings it introduces. Treat an assistant’s explanation, summary, or self-review as a clue for investigation rather than as evidence of correctness.
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How do I reconstruct the intended scope before reading individual lines?
Start with the requested behavior and the boundaries of the change. You should know which components are supposed to change, which files are expected, which data or users are affected, and which paths are security-sensitive before judging implementation details.
Map the request to the repository
- Write a short statement of the required behavior in terms of inputs, outputs, side effects, and failure conditions.
- List the services, modules, endpoints, jobs, schemas, and configuration areas that can implement that behavior.
- Identify authentication, authorization, tenant isolation, payment, personal-data, file-upload, command-execution, and deployment paths touched by the request.
- Compare that map with every changed file. An unexpected file is a scope question, not a harmless detail.
Read more than source files
Inspect application code together with tests, lockfiles, generated files, build scripts, package scripts, CI/CD configuration, container definitions, deployment manifests, and repository instruction files such as AGENTS.md or .cursorrules. An AI-generated change can alter its effective security posture without changing the main application module—for example by adding a workflow permission, a post-install script, or an instruction that tells an agent to disclose secrets.
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Perform an impact and trust-boundary review
For each changed data flow, ask where untrusted input enters, where identity and authorization are established, where data crosses a service or privilege boundary, and where output is rendered or executed. OWASP’s diff-based review approach calls for impact analysis, new-attack-vector analysis, trust-boundary checks, integration review, and security-regression checks. Record questions that cannot be answered from the diff and resolve them before approval.
What should I check for security omissions in the diff?
Read security controls as deliberately as the feature logic. AI output often produces a plausible happy path while omitting a check that an experienced maintainer would add.
Identity and authorization
- Confirm that authentication is required on every sensitive route and that the code uses the project’s established identity mechanism.
- Check authorization at the point where the protected object or action is selected, not only at an outer controller or user-interface layer.
- Test horizontal access, such as one user requesting another user’s record, and vertical access, such as a non-administrator invoking an administrative operation.
- Verify tenant, organization, role, and resource ownership checks after object lookup; do not rely on an identifier being difficult to guess.
Input, output, and error handling
- Trace every externally controlled value through parsing, validation, database queries, templates, logs, redirects, file paths, and command arguments.
- Require allow-list validation where the domain permits it, enforce size and range limits, and reject malformed or unexpected types.
- Check output encoding in the context where data is used, such as HTML, JavaScript, SQL, shell, URLs, or structured logs.
- Ensure errors do not reveal credentials, tokens, stack traces, internal paths, or cross-tenant data, while still producing useful diagnostics for operators.
Cryptography and data handling
Look for new encryption, signing, password, token, random-number, or key-storage code. Confirm that it uses the project’s approved primitives and key-management approach rather than a homemade scheme or a deprecated algorithm. Follow sensitive values through memory, logs, caches, analytics, backups, and responses; a feature can be secure at its entry point and still leak data later.
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Queries, files, and execution
Reject string-built queries when parameterized interfaces are available. Check file names, archive extraction, temporary-file permissions, path traversal, deserialization, template evaluation, and any invocation of a shell, interpreter, compiler, or subprocess. AI assistants may suggest packages or snippets that appear familiar but are nonexistent, abandoned, or unsafe; every such suggestion requires independent verification.
How do I verify dependencies suggested by an AI assistant?
Do not install a package solely because an assistant named it. Verify its existence, provenance, maintenance status, version policy, and known vulnerabilities before it enters the repository.
- Confirm that the exact package name exists in the intended registry and that it is the library the code actually needs, not a similarly named impostor.
- Inspect the project’s ownership, release history, license, maintenance activity, and publisher or organization provenance according to team policy.
- Review the dependency’s transitive tree and install or build scripts for network access, shell execution, credential handling, or unexpected file changes.
- Pin or constrain versions using the repository’s established lockfile and update policy. Review lockfile changes as code because they can add or replace many transitive packages.
- Check current advisories with the ecosystem’s approved tools, such as
npm audit,pip-audit,govulncheck, orcargo-audit, and consult the NVD, GitHub Advisory Database, or OSV where appropriate.
A clean advisory result does not prove that a dependency is trustworthy or suitable. It only means the checked databases and configuration did not report a matching issue at that time.
Can I trust an AI coding assistant’s tests?
No. Treat AI-written tests as claims about behavior that need independent challenge. They may faithfully assert the generated implementation while missing the requirement, delete difficult cases, weaken assertions, or replace a real integration with a convenient mock.
Inspect the test diff itself
- Look for deleted tests, changed expected values, broad matchers, disabled suites, skipped cases, and assertions that only check a function did not throw.
- Identify new mocks, fakes, fixtures, and stubs that bypass authentication, persistence, network behavior, queues, or serialization used in production.
- Check whether the test data exercises authorization decisions and failure paths, rather than only returning a successful response.
- Compare tests with the written requirement and with neighboring tests that establish the project’s intended conventions.
Add independent negative and adversarial cases
Write or request cases for invalid input, missing and expired credentials, malformed payloads, boundary values, duplicate requests, partial failures, retries, and concurrency. Include cross-user and cross-tenant access attempts where applicable. A test that passes because it mirrors the implementation is not independent assurance; at least some expected outcomes must come from requirements, security properties, or a separately reasoned reference.
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Passing tests cannot establish security on their own. Business-logic flaws, broken access control, and incorrect assumptions about deployment context can survive a completely green suite.
Which automated gates should run before I open the pull request?
Run the same deterministic gates used for human-written changes, on the complete proposed diff and with the repository’s supported versions. A normal review should include formatting, type checking, unit and integration tests, relevant build checks, static application security testing, software-composition analysis, and secret scanning.
Interpret each result correctly
- Formatting and type checks: catch consistency and interface mistakes, but do not evaluate business intent or authorization.
- Unit and integration tests: provide executable behavior checks; confirm that the selected tests exercise the changed paths and real integrations where needed.
- Build and packaging checks: expose compile-time, generated-code, container, and deployment failures that local unit tests may miss.
- SAST: can identify patterns such as injection risks or unsafe APIs, but it rarely understands the full permission model.
- Software-composition analysis: flags known dependency issues; it does not prove package provenance or fitness for purpose.
- Secret scanning: can detect many credential patterns, but reviewers must still inspect configuration, logs, fixtures, and generated artifacts for sensitive data.
Fix or consciously disposition every failure. Do not suppress a finding merely to obtain a green status; document why a false positive is safe and preserve the audit trail.
What should I inspect in CI, deployment, and agent files?
Treat operational and instruction files as production code. A small change in a workflow or container can grant more privilege than a large application change.
Workflow and build permissions
- Check new or expanded repository, cloud, package-registry, and deployment permissions.
- Look for unpinned actions or images, scripts that execute downloaded content, and steps that expose secrets to untrusted pull-request code.
- Review changes to branch protections, required checks, artifact publishing, release triggers, and environments with production access.
Network and shell behavior
Identify new outbound network calls, downloads, telemetry, shell commands, subprocesses, Docker capabilities, mounted credentials, and writable paths. Confirm that destinations are expected and that timeouts, authentication, and failure behavior are explicit. An apparently harmless test helper can become a supply-chain or secret-exfiltration path when it runs in CI.
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Agent instructions and prompt injection
Read instruction files and comments that an AI tool may consume. Flag directions that ask an agent to ignore repository policy, reveal secrets, weaken tests, disable security checks, or run arbitrary commands. Instructions are inputs to a tool, not authority to bypass code ownership or protected-branch controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When does an AI-generated change need specialist or owner approval?
Use the change’s risk, not the amount of generated text, to decide review depth. Lightweight feature work can follow the normal gates; changes that cross a sensitive boundary deserve named ownership and, where available, a security champion.
| Change profile | Review path | Ownership focus |
|---|---|---|
| Low-risk feature within an established module | Complete diff review, normal tests, formatting, type, build, SAST, dependency, and secret checks | Maintainer of the affected component |
| Authentication or authorization | All normal gates plus explicit negative tests and required owner approval | Identity or security owner |
| Cryptography or sensitive data handling | All normal gates plus specialist review of primitives, key flow, storage, and error behavior | Security or cryptography-qualified reviewer |
| CI/CD, infrastructure, deployment, or trust-boundary change | All normal gates plus workflow-permission, secret-exposure, and rollback review | Infrastructure owner or security champion |
Configure CODEOWNERS and protected branches so the required people cannot be bypassed accidentally. OWASP’s governance guidance is explicit: no AI-generated code is exempt from code review, security scanning, or testing.
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Make the provenance and approval path visible in the normal development record. Mark AI assistance in the commit or pull-request description, record the tool or model when policy requires it, and identify the developer who reviewed the complete change.
What the record should establish
- What behavior was requested and what the assistant generated or modified.
- Which files and risk areas were reviewed, including tests, dependencies, workflows, and instruction files.
- Which automated gates ran, their relevant versions or configurations, and how exceptions were handled.
- Who reviewed security-sensitive paths and who approved the final change.
- What follow-up work, limitations, or operational assumptions remain.
Do not approve from an AI summary alone. The approving developer must have read and understood the actual diff and be able to explain why it is correct, secure, and maintainable. The Home Office guidance describes this as full team accountability, traceable through commits, pull requests, reviews, and approvals.
What does a complete pre-pull-request review look like?
Use this sequence when you are the person about to submit the change:
- Define the contract: write the required behavior, affected components, inputs, outputs, failure modes, and sensitive paths.
- Inventory the change: inspect every file, including lockfiles, tests, build and deployment files, CI workflows, and AI instruction files.
- Trace trust boundaries: follow identity, authorization, untrusted data, secrets, files, queries, network calls, and process execution through the implementation.
- Verify dependencies: confirm package identity, provenance, maintenance, version controls, transitive changes, and current advisories before installation or approval.
- Challenge the tests: inspect deletions and mocks, compare assertions with requirements, and add negative, adversarial, boundary, and concurrency cases.
- Run the gates: execute formatting, type, build, unit, integration, SAST, composition, and secret checks used by the project.
- Escalate risk: obtain CODEOWNERS or specialist approval for authentication, authorization, cryptography, CI/CD, infrastructure, and other trust-boundary changes.
- Record and approve: disclose AI assistance as required, preserve results and exceptions, and approve only when a named developer accepts responsibility.
If any step cannot be completed, pause the pull request and state the unresolved question. “The assistant said it was safe” is not a review result, and “all tests passed” is not a substitute for understanding the change.
Frequently Asked Questions
Should I disclose which AI model produced the code?
Follow your team’s traceability policy. If it requires the tool or model, record that information in the commit or pull-request description alongside the human reviewer and the checks that ran; even when the model name is optional, disclose AI assistance when your organization requires it.
Can a second AI tool review the generated diff instead of a human?
A second tool can help find patterns, missing tests, or suspicious dependencies, but it cannot assume accountability for business logic or authorization context. Use automated review as an additional signal and retain a named developer who reads and approves the complete change.
What if the generated patch changes files outside the requested feature?
Stop and classify each extra change before proceeding. Revert unrelated edits where possible, or split them into separately reviewed changes so their ownership, tests, and security impact are not hidden inside the feature pull request.
How should I handle a dependency that appears in code but cannot be verified?
Do not install or merge it. Confirm the package name in the intended registry, investigate similarly named packages and provenance, and ask the author to replace it with an approved dependency or implement the behavior without it.
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