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A passing test suite or clean scanner report is only a starting point. Automated tools recognize known patterns; they do not reliably understand authorization, business rules, tenant boundaries, or hostile instructions hidden in repository content. The workflow below combines automated evidence with deliberate human review.
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The review standard: prove behavior, security, and provenance
AI-generated code can look polished while containing a wrong API, a missing authorization check, an unsafe default, or logic that only works for the example in the prompt. Treat the generated diff with the same scrutiny—or greater scrutiny—you would apply to code from an unknown contributor.
Use three independent questions:
- Behavior: Does the change implement the stated requirement across normal, boundary, and failure cases?
- Security: Does it preserve trust boundaries, protect data, and resist malicious or malformed input?
- Provenance: Are the dependencies, instructions, configuration values, and generated assumptions real, reviewed, and appropriate for this repository?
A human owner must understand and approve the result. OWASP’s Secure Coding with AI guidance puts it plainly: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.”
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A practical review sequence
| Stage | What you establish | Evidence to retain |
|---|---|---|
| Intent and boundaries | Requirement, affected users and services, data flows, and invariants | Issue or design reference, threat assumptions, acceptance criteria |
| Deterministic checks | Build validity and behavior under existing and newly added tests | Compiler output, test results, lint and analysis reports |
| Security diff review | Authorization, validation, data handling, cryptography, and error behavior | Reviewed diff, findings, remediations, reviewer identity |
| Assumption hunt | Behavior for malformed, repeated, concurrent, delayed, and partial inputs | Additional tests, threat notes, design decisions |
| Dependency and provenance review | Packages, versions, APIs, licenses, and configuration keys are legitimate | Lockfile review, package records, license and maintenance checks |
| Development-loop defense | Agent instructions and credentials cannot be abused by hostile repository content | Sandbox settings, token scope, action logs |
| Approval and audit | A qualified person accepts residual risk and owns the change | Explicit approval, tool and model version, audit trail |
1. Establish intent and boundaries before reading the implementation
Read the surrounding context
Start with the issue, requirements, architecture notes, threat model, and nearby code. Read the callers, data models, error conventions, and tests that existed before the generated change. A diff cannot tell you whether its behavior fits the system’s design.
Write down answers to these questions before deciding that the code is correct:
- Which users, services, jobs, or administrators can reach the changed entry point?
- What operation is allowed, and which object or tenant is it allowed to affect?
- What data enters and leaves the component, and where does it cross a trust boundary?
- Which invariants must remain true—for example, ownership, balance, uniqueness, ordering, or state transitions?
- What failure behavior is required: rollback, retry, quarantine, an explicit error, or an idempotent response?
- What does the requirement deliberately not cover?
Separate stated requirements from model assumptions
Compare names, comments, prompt text, and issue wording with actual behavior. Generated code often fills gaps with plausible assumptions about business rules, user roles, formats, or defaults. Mark each assumption as one of three categories:
- Specified: directly required by the issue, contract, or architecture.
- Established: demonstrated by existing code, tests, or a documented invariant.
- Unproven: inferred by the model or reviewer and requiring a decision or test.
Do not merge an unproven assumption merely because the implementation is tidy. Resolve it with the product or system owner, then encode the decision in a test, validation rule, or design note.
Map the trust boundaries
Draw a simple path from each external input to storage, network calls, templates, logs, and responses. Mark identity and authorization checks, parsing steps, transformations, and privileged operations. This map focuses review on places where a small omission has a large consequence.
2. Run deterministic checks before relying on judgment
Build and type-check the exact change
Compile or type-check the branch with the repository’s normal settings. Use the same build profile, feature flags, generated files, and environment assumptions used in deployment. A successful local build is not evidence if the production profile exercises different code.
Run the full relevant test ladder
- Run unit tests for changed functions and their direct callers.
- Run integration tests covering databases, queues, caches, identity providers, and external services touched by the change.
- Run end-to-end or regression tests for user-visible workflows and previously fixed defects.
- Run migration and rollback tests when schemas, data formats, or deployment steps change.
- Add tests for paths that are missing, deleted, skipped, or newly reachable. Do not accept a reduced test set simply because the generated code passes the remaining tests.
Include negative tests: invalid credentials, wrong-tenant objects, malformed payloads, expired tokens, duplicate requests, timeouts, and downstream failures. NIST’s broadly applicable techniques include automated testing, structural and black-box tests, historical tests, fuzzing, web-application scanning, and verification of included libraries and services.
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Use layered analysis
Run the project’s linters and static analysis, software-composition or dependency checks, and secret scanning. Use hardcoded-secret heuristics and, where appropriate, dynamic or web-application scanners. Review findings rather than suppressing them automatically; record why a warning is false positive or how it was fixed.
“Always run automated tests and static analysis tools first,” advises GitHub’s documentation. That order gives the human reviewer fast feedback about syntax, types, obvious defects, and known vulnerability patterns before the harder contextual questions begin.
Interpret a clean report correctly
A clean report means only that the configured tools found no issues in the patterns and paths they understand. It does not prove that a caller is authorized, a transaction is atomic, a tenant identifier cannot be substituted, or a business rule is correct. Continue to manual review even when every automated check is green.
3. Review the diff for security omissions
Authentication, sessions, and authorization
Check that authentication establishes the intended identity and that authorization is evaluated for every sensitive action. Verify the object and tenant being accessed, not merely the role attached to the request. Look for endpoints that rely on a client-supplied user ID, an earlier middleware check that does not cover this route, or a privileged helper called from a newly public path.
Inspect session creation, renewal, expiration, logout, and replay behavior. Confirm that failure paths do not accidentally continue with an anonymous or partially initialized identity.
Input validation and parsing
Validate untrusted data at the trust boundary for type, length, range, encoding, and allowed values. Ensure validation happens before authorization decisions that depend on the parsed value. Queries must use parameterized interfaces rather than string-built commands. Review file names, paths, archive contents, redirects, template variables, and upload metadata as hostile input.
Data flow, logging, and errors
Trace secrets, personal data, tokens, and tenant identifiers through memory, logs, telemetry, caches, responses, and third-party calls. Error messages should help the legitimate caller without exposing credentials, stack traces, internal identifiers, or sensitive records. Check both handled exceptions and timeouts, cancellation, partial writes, and retries.
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Cryptography and deserialization
Confirm that cryptographic algorithms, modes, key sizes, randomness, key storage, and certificate validation match the project’s approved practices. Reject deprecated or invented APIs. Treat serialized input as hostile: constrain types and sizes, authenticate protected data, and avoid dangerous object rehydration. Generated code may select a familiar-looking library or method that does not exist or is unsafe for the language version.
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Files, uploads, and public endpoints
For uploads and filesystem operations, check size limits, content verification, temporary-file permissions, path normalization, overwrites, cleanup, and malware or quarantine handling where required. For public endpoints, inspect rate limits, CORS, caching, request size, and exposure of internal errors. OWASP identifies missing authorization and input validation, weak or deprecated cryptography, string-built queries, and unfamiliar or hallucinated dependencies as recurring concerns in AI-generated code.
4. Hunt hidden assumptions with adversarial cases
After reading the happy path, deliberately vary one condition at a time. Ask what the function does when an input is:
- Empty, null, duplicated, oversized, truncated, malformed, or encoded differently.
- Out of numeric range, negative, at a time-zone boundary, or subject to daylight-saving changes.
- Repeated, reordered, delayed, retried, canceled, or delivered concurrently.
- Partially authorized, from another tenant, or associated with a deleted object.
- Produced by an older client, a newer server, or a partially completed migration.
- Connected to a slow, unavailable, inconsistent, or malicious downstream service.
Check invariants, not just examples
A narrow unit test may prove that one input returns one expected value while the implementation violates a system invariant. Examine transaction boundaries and race conditions: can two requests spend the same balance, create duplicate records, or observe an intermediate state? Confirm idempotency keys and retry behavior. Ensure cleanup and rollback happen after every partial failure.
Compare names and comments with effects
Generated names such as isAdmin, safePath, or validated are labels, not guarantees. Follow the value to its sink and verify the actual predicate. Comments can describe an intended policy that the code never enforces; treat executable behavior as the thing to test and correct.
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For each unproven assumption, add a test that would fail if the assumption were wrong. Property-based or fuzz testing is useful for parsers, encoders, numeric boundaries, and state machines. Keep regression tests for every discovered defect so a later generated change cannot silently reintroduce it.
5. Validate dependencies, APIs, and provenance
List every new import, package, service, API method, version, license, and configuration key. Confirm that each exists in the project’s approved registries or official documentation and that its behavior matches the call site. Never install a package merely because a model named it.
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Review the lockfile and transitive graph
Inspect lockfile changes, transitive dependencies, maintainer status, known advisories, and license compatibility. A tiny source diff can pull in a large dependency tree. Check whether the package is needed at all, whether an existing internal utility already provides the function, and whether the selected version is supported by the runtime.
Verify configuration and operational assumptions
Every new environment variable, permission, queue, database field, feature flag, and service endpoint needs an owner and a deployment plan. Confirm safe defaults when a key is absent, rotated, malformed, or unavailable. Ensure secrets are supplied by the approved secret store rather than committed to source or logs.
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Models can invent methods, options, status codes, or package names that look credible. Compile-time checks catch some errors; runtime paths, version-specific behavior, licensing, and failure semantics still require documentation and integration verification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Defend the development loop against indirect prompt injection
Coding agents may ingest issue bodies, pull requests, review comments, README files, dependency metadata, and fetched documentation. Those materials can contain instructions aimed at the agent rather than requirements for your software. Treat repository instruction files and external text as untrusted content.
Review instructions as code
Inspect agent configuration, repository guidance, scripts, and automation for requests to exfiltrate secrets, weaken tests, disable security checks, or make unrelated changes. Hidden Unicode characters and visually deceptive text deserve special attention. Separate authoritative requirements from content the agent merely retrieved.
Constrain execution
- Run agents in a sandbox or disposable environment without production credentials.
- Use short-lived, narrowly scoped tokens and deny unnecessary network, filesystem, and deployment access.
- Require human confirmation before package installation, secret access, destructive commands, or pushes to protected branches.
- Log prompts, tool calls, files changed, commands run, and external destinations so an incident can be reconstructed.
These controls protect the development process itself; they do not replace review of the generated code.
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7. Apply risk-based approval gates
Review depth should increase with the consequence of failure and the number of trust boundaries crossed.
| Change characteristic | Minimum additional control |
|---|---|
| Purely internal refactor with unchanged interfaces | Build, relevant tests, static analysis, dependency and secret checks, and owner approval |
| Authentication, authorization, session, cryptography, parsing, or file-handling change | Focused security review, negative tests, and approval by a reviewer trained in the affected area |
| New public endpoint, third-party integration, data store, CORS rule, or network exposure | Threat-model update, end-to-end tests, dependency review, and explicit security sign-off |
| High-impact data, financial state, or cross-tenant operation | Independent review, concurrency and rollback tests, monitored rollout, and a documented rollback plan |
Use CODEOWNERS or an equivalent ownership mechanism to route sensitive paths to qualified reviewers. Block merges on critical findings according to the organization’s severity policy; AISVS gives CVSS 9.0 or an equivalent policy threshold as an example of a critical gate. Do not let a model’s confidence, a small diff, or schedule pressure lower the gate.
8. Record accountability and make the decision reproducible
Before merge, record the human owner, reviewers, AI tool and model version, checks run, exceptions accepted, and links to the requirement and threat assumptions. The approver should be able to explain the code’s behavior, security properties, dependencies, and operational consequences without asking the model to justify it.
OWASP’s guidance says to “Treat AI as a tool, not a colleague.” The practical implication is clear: the person who approves the change remains responsible for correctness, security, and maintainability. “The AI wrote it” is not an exemption from review.
A reusable AI-code review checklist
- Requirement, callers, data flows, trust boundaries, and invariants are documented.
- All new behavior is covered by appropriate unit, integration, end-to-end, migration, or regression tests.
- Missing, deleted, and skipped tests were investigated rather than accepted silently.
- Build, type-check, lint, static analysis, dependency checks, and secret scanning completed.
- Authentication, object-level authorization, tenant isolation, sessions, and privileged operations were traced end to end.
- Input validation, parameterized queries, output encoding, uploads, paths, deserialization, and redirects were checked.
- Cryptography, key handling, error messages, logs, caches, and telemetry do not leak or weaken protections.
- Empty, malformed, oversized, duplicated, concurrent, retried, delayed, unauthorized, and partial-failure cases have defined behavior.
- Every package, API, version, license, configuration key, and lockfile change is verified.
- Agent instructions and repository text were checked for hostile or deceptive directives.
- Execution used a sandbox and scoped credentials, with actions logged where agents were involved.
- A named human owner reviewed and explicitly approved the change, with an audit trail.
What to do when a review finds a problem
When the defect is local
Write a failing regression test, fix the implementation, rerun the complete relevant checks, and update the review record. Do not ask the model to regenerate the entire feature without preserving the failing case and the original requirement.
When the requirement is ambiguous
Pause implementation, obtain a decision from the product or system owner, and encode the decision in acceptance criteria and tests. Ambiguity is a design issue, not a reason to accept the model’s default.
When a dependency or instruction is suspicious
Remove the package or isolate the agent run, rotate any exposed credentials, inspect logs and lockfile changes, and involve the appropriate security owner. Re-run checks from a clean environment before reconsidering the change.
When scanners disagree with context
Document the data flow and reviewer reasoning, then suppress only the specific false positive under the project’s normal process. A suppression without an explanation turns a known uncertainty into invisible risk.
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AI-generated code becomes acceptable through evidence, not authorship. Establish intent, prove behavior with deterministic checks, manually examine trust boundaries and hidden assumptions, verify dependencies and the development loop, apply a risk-based gate, and require accountable human approval. Automated scanners are valuable—but human context is what catches the flaws they cannot see.
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