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Review AI-generated code as untrusted input, not as a trusted contribution. Establish what the change must do, then verify it with compilation, tests, static analysis, dependency and secret checks, followed by a human inspection of authorization, data flow, failure cases, and business rules. A named engineer who understands the change must approve it before merge.
Adopt the right trust model first
Code produced by a chatbot, autocomplete tool, or coding agent can look idiomatic while containing incorrect assumptions, missing controls, invented APIs, or unsafe defaults. Its origin does not make it malicious, but it does mean you cannot infer intent, provenance, or correctness from its appearance. Treat every generated patch like code from an unknown contributor—and apply extra scrutiny when the tool had access to repository files, issue text, pull requests, documentation, or credentials.
There is no authoritative, universal defect rate for AI-written code. Results vary by model, language, task, prompt, and evaluation method. The useful conclusion is procedural: automated checks and human review are both required. OWASP’s Secure Coding with AI guidance summarizes the accountability rule this way: “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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1. Establish intent and boundaries before judging the diff
Start with the requirement, not the generated implementation. Read the issue or task, acceptance criteria, architecture notes, threat model, migration plan, and the surrounding code that calls or consumes the change. GitHub’s code-review guidance recommends checking alignment with project requirements and asking what assumptions the change makes about business logic, design preferences, and user behavior.
Write down the intended behavior
- State the user or system outcome in one sentence.
- List the actors, services, jobs, and interfaces that can invoke the code.
- Identify data entering and leaving each trust boundary, including files, queues, webhooks, databases, and third-party APIs.
- Record invariants that must remain true, such as ownership checks, uniqueness, accounting totals, or state-transition rules.
- Define what must happen on success, validation failure, timeout, cancellation, retry, and partial failure.
Compare scope with the requested change
Generated patches often change more than the task requires. Look for unrelated refactors, altered configuration, new telemetry, broad permission changes, disabled tests, and lockfile modifications. A small feature that adds a public endpoint, a new integration, a data store, CORS exposure, or a network path deserves a higher review tier than a local formatting change.
Map the trust boundaries
Mark every transition from an untrusted source to a privileged operation: HTTP requests to application code, uploaded files to parsers, messages to workers, user identifiers to database queries, and model output to shell commands or configuration. For each transition, identify the validation, authentication, authorization, encoding, and logging required before data is used.
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Run reproducible checks on the exact commit under review. GitHub recommends running automated tests and static analysis first; this quickly exposes syntax errors, type mismatches, broken contracts, and obvious security patterns before a reviewer spends time reasoning about design.
| Check | What it can establish | What still needs human judgment |
|---|---|---|
| Compile or type-check | The change satisfies language and declared type constraints. | Whether the types represent the right business concepts or trust levels. |
| Unit tests | Examples of isolated behavior pass. | Whether important paths, invariants, and adversarial inputs are missing. |
| Integration and end-to-end tests | Components work across real boundaries and workflows. | Whether authorization, tenancy, retries, and failure recovery are complete. |
| Regression and migration tests | Existing behavior and data transitions remain compatible. | Whether old records, rollback, and mixed-version deployments are safe. |
| Static analysis and linters | Known coding and security patterns are flagged consistently. | Contextual flaws, business logic errors, and incorrect trust assumptions. |
| Software-composition analysis | Declared and transitive dependencies can be checked for vulnerabilities and licenses. | Whether a package is necessary, authentic, maintained, and correctly configured. |
| Secret scanning | Known credential and token patterns are detected in code and history. | Whether a value is sensitive despite not matching a heuristic, and whether exposed secrets were revoked. |
| Fuzzing and web-application scanning | Malformed inputs and common runtime weaknesses receive automated probing. | Whether discovered behavior violates product rules or authorization boundaries. |
NIST lists threat modeling, automated testing, static scanning, hardcoded-secret heuristics, black-box and structural tests, historical tests, fuzzing, web-application scanners, and verification of included libraries and services as broadly applicable techniques. Use the techniques appropriate to the system; no single scan is a complete review.
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Do not accept deleted coverage
Inspect test changes as carefully as production code. A generated patch may remove, skip, weaken, or narrow a test to make the suite pass. Restore tests that encode a required behavior and add cases for paths the implementation introduces. Test both expected results and security properties, such as “a caller cannot read another tenant’s object,” rather than only checking that a request returns a successful status.
3. Manually inspect the security-critical paths
After deterministic checks, read the diff line by line with the surrounding code open. 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.
Authentication and session handling
- Confirm that authentication is required wherever the threat model says it is, including alternate routes, background jobs, and administrative endpoints.
- Check token verification, issuer and audience validation, expiration, rotation, revocation, and clock-skew handling.
- For browser sessions, inspect cookie flags, session fixation defenses, logout invalidation, cross-site request protections, and privilege changes.
- Ensure failures do not fall back to an anonymous or default identity.
Authorization and object ownership
Authentication answers who the caller is; authorization answers whether that caller may perform this action on this object. Verify the check at the point of use, not only in a controller or user interface. Test read, create, update, delete, export, and administrative actions separately. Check tenant or organization boundaries, role changes during a session, indirect object references, bulk operations, and alternate identifiers. A route that validates a user but accepts an arbitrary record ID can still expose another customer’s data.
Input validation and output encoding
Validate at each trust boundary using an allowlist and an explicit size, type, range, and format policy. Consider empty, duplicated, oversized, malformed, encoded, and nested values. Parameterize database queries instead of concatenating strings. Encode output for its destination—HTML, JavaScript, SQL, a shell, a log, a URL, or a file format—rather than assuming one sanitizer fits all contexts.
Deserialization, file handling, and parsers
Inspect any code that reconstructs objects from JSON, YAML, XML, binary data, or language-native serialization. Prefer safe, constrained formats and reject unexpected classes or fields. For uploads, enforce size and content limits, generate server-side names, store outside executable paths, verify type from content rather than only a filename, and prevent path traversal. Review archive extraction for traversal and decompression-bomb risks.
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Cryptography and sensitive data
Check that the implementation uses a current, well-supported library and an appropriate authenticated encryption, password-hashing, signing, or key-management primitive for the task. Look for hardcoded keys, predictable randomness, reused nonces, disabled certificate verification, weak fallback algorithms, and secrets written to logs or error responses. Confirm key rotation and failure behavior rather than assuming a library default is suitable.
Error handling and observability
Trace exceptions through every layer. External responses should reveal only what the caller needs; stack traces, SQL fragments, tokens, personal data, and internal paths belong in protected logs, if anywhere. Check that logs are access-controlled, tamper-evident where required, and free of secrets. Make sure a caught exception does not silently convert a failed authorization, transaction, or verification step into success.
State, transactions, and concurrency
Review transaction boundaries, isolation, locking, and retry behavior. Ask whether two requests can both pass a check before either writes, whether a retry duplicates an operation, and whether an external side effect occurs before a database commit. Use idempotency keys or durable state where the operation requires them. Confirm that timeouts and cancellation cannot leave a record in an impossible intermediate state.
4. Hunt hidden assumptions with adversarial questions
Plausible-looking logic often fails outside the narrow example used to generate it. For every new function or workflow, deliberately vary the inputs and operating conditions:
| Condition | Question to answer |
|---|---|
| Empty or null | Does the code reject it, apply a documented default, or accidentally treat it as an authenticated or valid value? |
| Duplicate | Is the operation idempotent, or can a retry create duplicate records, charges, messages, or permissions? |
| Oversized | Are memory, CPU, storage, and downstream limits enforced before expensive work? |
| Malformed or unexpected encoding | Do all layers interpret the value consistently, or can validation and use see different representations? |
| Unauthorized or cross-tenant | Is access denied for every operation and identifier variant? |
| Concurrent | Can interleaving requests violate uniqueness, balances, state transitions, or ownership? |
| Delayed or out of order | Does a stale event overwrite newer state, and is ordering or versioning enforced? |
| Partial failure | What happens if one service, write, or external call succeeds while another fails? |
| Clock and locale variation | Are time zones, daylight-saving changes, leap days, locale formatting, and clock skew handled explicitly? |
| Numeric extremes | What happens at zero, negative values, maximum integers, decimal precision limits, and unit conversions? |
Compare names, comments, and documentation with actual behavior. A function called isOwner may only check that a record exists; a comment promising atomicity may sit above a sequence of separate writes. Treat the executable behavior and its invariants as authoritative, then correct misleading names or documentation.
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Check recovery, not only the happy path
Trace how the system recovers from a failed migration, revoked credential, unavailable dependency, full disk, exhausted connection pool, or repeated message. Verify that retries have bounded backoff, that dead-letter or repair procedures exist where needed, and that a failure cannot bypass a security check on the next attempt.
5. Validate every dependency and its provenance
Do not install a package merely because a model named it. For every import, API, version, configuration key, and service, confirm that it exists, is the intended project, and is compatible with the application’s runtime and license policy. AI tools can hallucinate package names, methods, options, and version combinations that look credible.
- Review direct and transitive dependency changes in the lockfile.
- Check maintenance activity, supported versions, known vulnerabilities, and license obligations.
- Verify that an API’s semantics match the code’s assumptions, especially around authentication, timeouts, pagination, and error handling.
- Remove unused packages and avoid adding a large dependency for a small operation.
- Pin or constrain versions according to the project’s reproducibility policy and record why an exception is permitted.
Inspect generated configuration as well as source code. A new environment variable may be absent in production, a default may enable a debug mode, or a client may trust all certificates when a setting is missing. Fail closed for security controls and fail visibly for required configuration.
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 can ingest instructions that were not written by the person operating them. Issue bodies, pull requests, review comments, README files, dependency metadata, and fetched documentation may contain text designed to make an agent reveal secrets, weaken tests, run commands, or alter its goals. Treat repository instructions as untrusted input to the same degree as source data.
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Read agent configuration, contribution guides, build scripts, comments, and documentation that the tool is likely to consume. Look for hidden Unicode, visually deceptive characters, encoded commands, requests to ignore policy, and instructions unrelated to the task. Keep trusted operating instructions separate from repository content and require an explicit human decision before executing novel commands or changing security settings.
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Constrain the agent’s environment
- Run agents in a sandbox or disposable workspace without production credentials.
- Use scoped, short-lived tokens with only the permissions needed for the task.
- Restrict network access and require approval for package installation, data export, deployment, and destructive commands.
- Log prompts, tool calls, file changes, approvals, and outputs so an incident can be reconstructed.
- Regenerate or revoke credentials if an agent may have exposed them, even when no obvious secret appears in the final diff.
These controls protect the loop in which code is produced, not just the code that eventually reaches production.
7. Use risk-based approval gates
Not every patch requires the same ceremony, but every patch needs an accountable reviewer. Classify the change by potential impact and raise the gate when it crosses a trust boundary or changes security-sensitive behavior.
Examples of high-risk changes
- New public or administrative endpoints.
- Authentication, session, authorization, or identity-provider changes.
- Cryptography, secret storage, payment, health, or personal-data handling.
- File upload, deserialization, template rendering, shell execution, or query construction.
- New third-party integrations, data stores, CORS rules, network exposure, or cloud permissions.
- Changes to migrations, tenancy boundaries, audit logging, or deployment configuration.
Use CODEOWNERS or a trained security reviewer for the areas your organization designates as sensitive. Set explicit merge policies for scanner findings. AISVS gives CVSS 9.0 or an equivalent organizational threshold as an example of a critical gate; your policy should define severity, exploitability, exceptions, remediation deadlines, and who can approve a waiver.
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Separate automated gates from human gates
Automated gates should block what they can measure reliably: failed builds, critical known vulnerabilities, exposed secrets, policy violations, and required test failures. Human gates should decide whether the implementation satisfies the threat model, preserves business invariants, uses appropriate dependencies, and behaves safely under unusual conditions. Record the rationale for exceptions rather than turning a warning into an undocumented bypass.
8. Record accountability and provenance
Assign one human owner who understands the change and is responsible for correctness, security, and maintainability. The owner should be able to explain the requirement, the trust boundaries, the tests run, the remaining risks, and the rollback plan without asking the model to justify its own output.
Retain an audit trail containing the approver, the AI tool and model version, the reviewed commit, automated-check results, dependency and secret-scan reports, risk classification, and any approved exceptions. “The AI wrote it” is not an exemption from review. OWASP’s guidance puts the operational principle plainly: “Treat AI as a tool, not a colleague.”
A practical review checklist
- Read the requirement, architecture notes, threat model, and surrounding code.
- State the intended behavior, actors, data flows, invariants, and failure outcomes.
- Inspect the complete diff, including tests, configuration, generated files, and lockfiles.
- Compile or type-check the exact commit.
- Run unit, integration, end-to-end, regression, and migration tests; restore or add missing coverage.
- Run static analysis, linters, software-composition analysis, secret scanning, and relevant fuzzing or web scanning.
- Review authentication, sessions, authorization, tenancy, input validation, encoding, queries, deserialization, uploads, cryptography, errors, logging, and transactions.
- Exercise empty, duplicate, oversized, malformed, concurrent, delayed, retried, unauthorized, cross-tenant, and partially failed cases.
- Verify every dependency, API, version, license, configuration key, and transitive package.
- Inspect repository instructions and fetched content for prompt injection or hidden Unicode.
- Confirm the agent used a sandbox, least-privilege credentials, and logged actions.
- Apply the risk-based approval policy and obtain the designated human approval.
- Record the model and tool provenance, evidence reviewed, unresolved risks, and rollback plan.
What a clean review result means
A clean result means the change has passed the checks and reasoning appropriate to its risk; it does not prove that no defect exists. Automated tools miss context, trust-boundary errors, and business-logic flaws, while human reviewers can miss reproducible issues that a test or scanner would catch. The strongest process uses both, keeps the scope and assumptions visible, and makes one accountable engineer responsible for the final decision.
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