Review AI-written code as untrusted code from an unknown contributor. Read the entire diff, trace data across trust boundaries, verify authentication and authorization, inspect dependencies and secrets, test abuse cases, scan the application and its infrastructure, and require an accountable human approval. A green test suite or clean scanner report is evidence to investigate—not proof that the change is secure.
Why AI-generated code needs a different review posture
Code produced by a coding assistant can be useful, but the tool does not own the security consequences of its output. It may reproduce an obsolete pattern, invent a package, misunderstand the application’s tenancy model, omit an authorization check, or alter tests so that an unsafe behavior appears correct. The same risks apply whether a developer accepted one suggested function or an agent changed many files.
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OWASP’s secure-coding guidance for AI states that AI-generated code must have a human owner. That owner needs enough context to explain the change, identify its trust boundaries, and accept or reject the residual risk. Treat the author’s explanation, the model’s confidence, and the volume of generated tests as clues, not assurances.
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Start with a risk-based triage
Before reading individual lines, determine what the change can affect. The review depth, escalation path, and merge threshold should be set by the most sensitive area touched, not by the apparent size of the diff.
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High-risk surfaces
- Authentication, sessions, password resets, tokens, and cryptography.
- Authorization, tenant isolation, object ownership, administrator functions, and background jobs.
- Public endpoints, webhooks, file uploads, parsers, deserializers, templates, and shell or query construction.
- Third-party integrations, outbound requests, data stores, caches, queues, and message consumers.
- CORS, security headers, rate limits, logging, and error responses.
- CI/CD workflows, container images, infrastructure-as-code, IAM policies, buckets, security groups, and deployment credentials.
- Agent instruction files such as
AGENTS.md, workflow rules, hooks, and repository policy files.
Route changes in these areas to a security champion or a reviewer trained in application security. For especially sensitive files, require two-person review or explicit security-team sign-off.
Record the scope
Write down which repositories, services, environments, data classes, and deployment paths are included. Note whether the change is reachable from the public internet, whether it handles personal or financial data, and whether a failure could cross tenant boundaries. This scope determines which automated gates must run and who can approve an exception.
Read the complete change before trusting any explanation
Open the full diff and the surrounding implementation. Generated code often looks reasonable in isolation while violating an invariant enforced elsewhere. Compare the claimed intent with the actual files changed, including tests, configuration, migrations, dependency manifests, generated artifacts, workflow files, and documentation that controls an agent.
Questions to answer for every changed function
- What data enters the function, and who controls each value?
- What validation, normalization, and length or type limits are applied?
- What data leaves the function: a query, template, command, file path, redirect, log, or network request?
- Which identity and permissions are available at that point?
- Which error paths, retries, timeouts, and fallbacks execute when a dependency fails?
- What assumptions does downstream code make about encoding, ownership, or transaction state?
Do not approve a change that its owner cannot explain in ordinary language. Ask the author to diagram the request path, data stores, queues, and external services. Disagreement between that explanation and the implementation is a reason to pause the merge.
Trace untrusted input through every sink
Security review becomes concrete when every source is followed to every sensitive operation. Mark the source, transformations, validation point, and sink. Inputs include more than HTTP fields: headers, uploaded files, serialized messages, environment variables, webhook bodies, log content, model output, and tool output can all be attacker-influenced.
Injection and unsafe interpretation
- SQL or NoSQL injection: confirm parameterized queries and safe query builders. Reject string concatenation that places request data into query syntax.
- Command injection: prefer an argument array and a fixed executable. If a shell is unavoidable, enforce an allowlist and safe escaping; never pass model or user text directly to a shell.
- Template or HTML injection: encode output for its actual context. Sanitizing input once is not a substitute for context-aware output encoding.
- Deserialization: use data-only formats and explicit schemas. Do not deserialize attacker-controlled classes or invoke arbitrary constructors.
Server-side request forgery and outbound calls
For code that fetches a URL, validate the scheme, resolve and check the destination, block loopback and private network ranges where appropriate, limit redirects, and set strict timeouts and response-size limits. Recheck the destination after DNS resolution when the threat model includes rebinding. Do not assume that a URL supplied by a trusted-looking webhook is safe.
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Paths, files, and uploads
Normalize and constrain user-controlled paths, prevent traversal outside an intended directory, and avoid using the original filename as an authorization decision. For uploads, enforce size and type limits, store outside executable web roots, generate server-side names, scan when required, and ensure decompression cannot create an archive bomb. Review temporary-file permissions and cleanup behavior.
Model and tool output
Output from a language model, plug-in, browser, or repository tool is untrusted data. Parse it with a strict schema, apply authorization independently, and escape it before inserting it into code, commands, markup, or configuration. A model-generated instruction that says to disable a check is not a security exception.
Verify authentication, authorization, and tenant isolation
Authentication proves who is calling; authorization decides what that identity may do. Check both on every route, resolver, message consumer, scheduled job, and administrative operation. A scanner may not know that user A must never read user B’s invoice, so context-dependent authorization requires deliberate review.
Session and token checks
- Confirm tokens are verified for signature, issuer, audience, expiry, and intended algorithm.
- Check session rotation after login or privilege change, secure cookie attributes, logout and revocation behavior, and replay protections where needed.
- Ensure password-reset and account-recovery tokens are single-use, time-limited, and not disclosed in logs or referer data.
- Verify failure paths fail closed. An unavailable policy service, malformed claim, or missing tenant identifier must not become implicit administrator access.
Object-level and function-level authorization
For every read, update, delete, export, and administrative action, identify the policy decision and the object being protected. Test an authenticated user accessing another user’s identifier, a member crossing tenant boundaries, and a lower-privilege account calling an administrator endpoint directly. Repeat the checks in background jobs, bulk APIs, GraphQL resolvers, and event consumers; these paths often bypass the middleware used by ordinary web requests.
Check dependencies and the software supply chain
AI suggestions can name a package that does not exist, confuse similarly named projects, or select a version with known vulnerabilities. Before installation or merge, verify the package identity, repository provenance, maintainer or publisher, license policy, and the version’s integrity metadata. Use the project’s official registry and lockfile rather than copying an unverified command from generated text.
Dependency review checklist
- Confirm every direct and transitive package is present in the intended ecosystem and is actually required.
- Pin or constrain versions according to organizational policy, and review lockfile changes for unexpected additions or upgrades.
- Run software-composition analysis against current vulnerability databases and examine reachability rather than dismissing a finding automatically.
- Check install scripts, native extensions, permissions, and network access for packages added by the change.
- Plan a response for a newly disclosed vulnerability: upgrade, configure a mitigation, isolate the component, or document an authorized exception.
A dependency scan is a point-in-time signal. It cannot establish that a package is trustworthy, that a vulnerable code path is unreachable, or that a malicious maintainer will not publish a later release.
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Inspect secrets and sensitive data exposure
Search the diff, repository history, test fixtures, logs, configuration, and generated files for API keys, passwords, private keys, tokens, connection strings, and personal data. Check both obvious literals and encoded or split values. Remove exposed credentials from the code, rotate them through the owning provider, and treat them as compromised even if the commit is deleted later.
Review what the AI service can see
Coding assistants may receive open files, terminal output, stack traces, repository context, or pasted production data. Identify the provider’s retention and training settings, access controls, regional requirements, and audit options. Exclude credential directories, customer records, private keys, and other sensitive paths where the tool supports exclusions. Redaction in the interface does not protect data that was already copied into a prompt or log.
Logging and error handling
Check that exceptions, request bodies, authorization headers, reset links, database errors, and model prompts are not logged at a level accessible to broad audiences. Logs should preserve enough context for incident response without becoming a second data store for secrets or personal information. Generic client errors should not reveal stack traces, SQL, filesystem paths, or internal hostnames.
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Tests can be fabricated, weakened, or deleted while remaining green. Compare the test diff with the previous suite and investigate removed cases, reduced assertions, broad mocks, and snapshots that merely encode the new behavior. A mock that replaces the policy engine, database authorization, or TLS boundary may prove nothing about the real security property.
Add adversarial and negative cases
- Malformed, oversized, duplicated, and unexpected-type inputs.
- Expired, forged, audience-mismatched, and revoked tokens.
- Boundary values, Unicode and canonicalization variants, and alternate encodings.
- Cross-user and cross-tenant object identifiers.
- Concurrent requests that test race conditions, replay, and double spending.
- Dependency timeouts, partial failures, retries, and queue redelivery.
- Unauthorized operations attempted through background jobs, bulk endpoints, and alternate API versions.
Keep security tests close to the boundary they protect, but also run an end-to-end test that exercises the real authentication, authorization, storage, and network configuration. A test that only checks a generated function’s return value cannot establish that the production route is protected.
Review CI/CD, containers, and infrastructure changes
Infrastructure code is executable security policy. Examine cloud roles, network rules, storage permissions, encryption settings, audit logging, container files, deployment manifests, and pipeline definitions with the same care as application code.
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Common high-impact mistakes
- Public buckets, registries, dashboards, or security groups introduced for convenience.
- IAM roles widened from a resource-specific permission to an account-wide wildcard.
- Encryption, key rotation, image signing, vulnerability scanning, or audit logging disabled.
- Container images and CI actions referenced by mutable tags instead of trusted digests or constrained versions.
- Secrets placed in workflow variables, build arguments, artifacts, command lines, or pull-request comments.
- Pull-request workflows executing untrusted code with privileged tokens, write access, or production credentials.
Inspect changes to workflow triggers and permissions before looking at the application test result. A harmless-looking documentation edit can alter an agent rule or cause a privileged job to run attacker-controlled code.
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Run static application-security testing, software-composition analysis, secret scanning, and infrastructure-as-code scanning on every AI-generated pull request. Add dynamic or interactive testing when the system’s exposure and risk justify it. Set policy thresholds that block critical findings and document any approved exception with an owner, rationale, compensating control, expiry, and follow-up issue.
| Control | Best at finding | Human follow-up required |
|---|---|---|
| SAST | Known insecure coding patterns, tainted flows, and dangerous API use | Confirm reachability, data sensitivity, exploitability, and business impact |
| SCA | Known vulnerabilities and license issues in dependencies | Verify package identity, affected versions, reachability, and remediation |
| Secret scanning | Credential-like strings and known key formats | Determine validity, rotate exposed secrets, and inspect logs and history |
| IaC scanning | Public exposure, excessive permissions, and insecure defaults | Compare with intended architecture and compensating controls |
| DAST or IAST | Runtime behavior, reachable endpoints, and configuration interactions | Cover authenticated roles, tenant boundaries, abuse cases, and false negatives |
OWASP summarizes the division of labor this way: "Automated scanners find known patterns; human review finds the flaws that need context — a missing authorization check, a business-logic bypass, a trust boundary drawn in the wrong place." Spend automation time on repetitive detection and human time on architecture, authorization, business rules, cryptographic choices, error handling, and abuse scenarios.
Handle AI-specific traps deliberately
Hallucinated or stale packages
Do not run an install command merely because it appears plausible. Confirm the package in the intended registry, check its provenance and release history, and compare its API with official documentation. If the package cannot be verified, remove it and implement the feature with an established dependency or a small, reviewable local component.
Indirect prompt injection
Repository issues, pull-request comments, README files, changelogs, logs, and fetched pages can contain instructions aimed at manipulating an agent. Treat those materials as untrusted input. Review every resulting edit, especially requests to disable scanners, reveal secrets, weaken tests, or modify instruction files. The fact that text is inside the repository does not make it an authorized command.
Test fabrication and deletion
Ask for the previous test count, inspect the diff for deleted cases, and require a rationale for changed assertions. Look for tests that assert a bug as the expected result, replace a real security service with a permissive mock, or skip the failure branch. Recreate a few negative tests manually rather than accepting a model-generated suite at face value.
Context leakage
Review terminal transcripts, prompt history, editor context, and assistant settings for credentials, personal data, proprietary algorithms, and architecture details. Minimize the context sent to the service, use approved enterprise controls where available, and train developers not to paste production records into prompts.
Instruction-file changes
Changes to AGENTS.md, workflow rules, hooks, and policy files can alter future agent behavior or bypass review. Give these files the same protected status as CI configuration. Require explicit ownership and review, and record why a rule changed.
Apply standards to make the process repeatable
NIST published SP 800-218A on July 26, 2024. It augments Secure Software Development Framework (SSDF) 1.1 with practices specific to generative AI and dual-use foundation models. Use the profile alongside normal SSDF practices, not as a replacement for them. In practice, that means keeping ordinary secure-development controls while adding governance for model provenance, prompt and context handling, generated artifacts, and AI-specific testing.
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OWASP AISVS Appendix C recommends qualified human review of AI-generated code; SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and SCA on every AI-generated pull request; and blocking critical findings unless an authorized human approves a written exception. OWASP SAMM similarly favors deterministic guardrails such as custom static-analysis rules, architectural-boundary checks, and pipeline gates, followed by regular review of authentication, authorization, secure communication, data protection, and logging.
Define an approval record and escalation path
A secure review ends with accountable evidence, not a thumbs-up comment. Record:
- The reviewer and, for sensitive changes, the second reviewer or security approver.
- The AI tool or model used, when that information is available.
- The files, services, environments, and data classes in scope.
- Automated checks run, versions or dates of vulnerability data, and unresolved findings.
- Manual scenarios examined, including authorization and abuse cases.
- Remediation commits, accepted risks, compensating controls, exception owner, and expiry date.
Escalate when the owner cannot explain the change, a trust boundary is unclear, a critical finding is unresolved, a dependency cannot be verified, a secret may have escaped, or a privileged workflow executes untrusted code. Do not merge first and promise to investigate later; the deployed artifact is the risk boundary.
A practical pull-request checklist
| Review question | Evidence to attach before approval |
|---|---|
| Was risk triaged by the most sensitive surface touched? | Scope note and named security reviewer when required |
| Can the owner explain every changed path and trust boundary? | Diff review notes or a short data-flow diagram |
| Are all untrusted inputs validated and safely encoded? | Negative tests for injection, traversal, SSRF, and deserialization |
| Are authentication, object authorization, and tenant checks fail-closed? | Cross-user, cross-tenant, expired-token, and privilege tests |
| Are dependencies genuine, constrained, and free of unaddressed known vulnerabilities? | Lockfile review and SCA result with disposition of findings |
| Are secrets and sensitive prompts or logs protected? | Secret-scan result, rotation record if needed, and assistant-context review |
| Could tests be deleted, weakened, or over-mocked? | Comparison with the prior suite and independent adversarial cases |
| Are CI, containers, and infrastructure least-privilege and auditable? | IaC and workflow scan plus review of permissions and triggers |
| Did every required automated gate run? | SAST, SCA, secret, IaC, and justified DAST or IAST results |
| Is the decision accountable? | Named approver, findings, remediation, and any written exception |
What a secure verdict looks like
Approval means the reviewer understands the code, the relevant threats were tested, automated findings were investigated, and remaining risk is owned explicitly. It does not mean that the model is trustworthy or that every undiscovered vulnerability has been eliminated. If the change affects authentication, authorization, cryptography, public input, sensitive data, or privileged infrastructure, use the stricter path even when the diff is small. That discipline lets teams benefit from faster code generation without treating generated output as a shortcut around secure engineering.
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