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Passing tests alone cannot establish that generated code is safe. The generator may have encoded a wrong assumption in both implementation and tests, mocked away the real failure, weakened an assertion, or skipped hostile inputs. Independent negative cases, contextual review, and risk-based scrutiny are what close those gaps.
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What makes AI-generated code different to review?
The security obligation does not shrink because an assistant wrote the code. OWASP’s Secure Coding with AI Cheat Sheet says generated code needs at least the scrutiny applied to code from an unknown contributor, and that human review remains necessary for security-relevant changes. The reviewer must be able to explain what the change does, why it is needed, and what must remain true after it ships.
Generation also changes the review surface. The visible diff may be only a few functions, while the assistant may have altered tests, package manifests, deployment files, agent instruction files, hooks, or continuous-integration definitions. Prompts and tool permissions can introduce risks outside the source code: sensitive files may have been sent to a provider, a command may have installed an untrusted package, or an agent may have gained network or credential access. Review both the resulting artifacts and the way they were produced.
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What a reviewer can and cannot infer
- A polished explanation or an AI-written review comment is a lead for investigation, not evidence that behavior is correct.
- A green test run demonstrates only that the executed tests passed under their current assumptions and environment.
- A clean static-analysis or dependency scan reduces some known risks but cannot determine whether a business operation is authorized.
- Code that looks conventional can still contain a wrong package name, an unsafe default, a missing state transition, or an omitted failure path.
Start with intent, scope, and risk
Do not begin by reading generated lines in isolation. First establish the contract against which they will be judged. Read the issue or request, design notes, API contract, data model, existing behavior, and the complete diff. Ask what files and interfaces should change, what must not change, and which assumptions are being introduced.
Questions to answer before detailed review
- What user-visible or operational behavior is this change supposed to provide?
- Which endpoints, jobs, commands, data stores, permissions, or external services are new or modified?
- Where do trust boundaries change—for example, from a browser to an API, from an API to a database, or from a build system to a deployment account?
- Did the change add a package, migration, workflow, hook, agent rule, prompt template, or infrastructure manifest?
- What invariants must remain true for existing users, tenants, and background jobs?
Treat agent instruction files and hooks as security-relevant configuration. OWASP guidance notes that they can influence future generations and therefore deserve scrutiny comparable to a CI/CD change. If the author or reviewer cannot describe the intended behavior and affected boundaries, stop the approval and resolve that uncertainty first.
Use risk to set review depth
Every change deserves ordinary automated gates, but not every change needs the same human effort. Increase scrutiny when the impact of a mistake is high, when a trust boundary is crossed, when authentication, authorization, cryptography, parsing, or deployment control is touched, when independent test coverage is weak, or when the AI tool had broad permissions or access to sensitive context.
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| Change characteristic | Why it raises risk | Review response |
|---|---|---|
| Authentication or authorization logic | A small mistake can expose another user or tenant. | Trace every decision to the protected action; add negative and cross-tenant tests; require a security-aware reviewer. |
| Cryptography or key handling | Familiar APIs can conceal an unsafe algorithm, mode, nonce, or failure path. | Verify the primitive, parameters, key lifecycle, rotation, and error behavior against approved standards. |
| Deserialization, parsers, or query construction | Untrusted data can become code execution, injection, or resource exhaustion. | Follow data from the boundary to use; test malformed, oversized, and nested inputs. |
| Deployment, IAM, CI/CD, or agent configuration | Configuration can grant durable privileges or alter what future code can do. | Use elevated review, least privilege, and specialist sign-off for critical files. |
| Low-impact internal logic with strong independent coverage | The consequence and uncertainty are smaller, though not zero. | Apply normal tests and static checks, then inspect assumptions and error handling. |
Trace data and control flow end to end
Pick each untrusted input and follow it from its entry point to storage, computation, external calls, and output. Do the same for the control that decides whether an action is allowed. Reviewing only the controller or user interface misses checks that are absent, delayed, or bypassable.
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Authentication and authorization
- Confirm that authentication establishes the identity used by downstream code and that failures fail closed.
- Check authorization at the point where the object is read or the action is performed, not only in a UI route or outer middleware.
- Test object ownership, tenant isolation, role boundaries, disabled accounts, expired credentials, and direct requests that skip the normal interface.
- Look for confused-deputy behavior in background jobs, webhooks, service accounts, and administrative endpoints.
Input validation and output encoding
Validation should happen before dangerous use and should match the actual parser and business rules. Check type, length, range, format, nesting depth, character handling, and duplicate-field behavior. Confirm that validation is applied consistently to alternate endpoints and asynchronous consumers. At output boundaries, use the encoding required for the destination—HTML, JavaScript, SQL parameters, shell arguments, logs, or HTTP headers—rather than relying on a generic “sanitize” helper.
Queries, dynamic execution, and deserialization
Reject string-built database queries when parameters or structured query APIs are available. Search for dynamic execution such as eval, shell interpolation, template evaluation, or runtime import decisions and establish whether an attacker can influence the value. For serialized data, identify the format, permitted types, object construction rules, and limits; never assume that a trusted producer remains trusted across a queue, file upload, cache, or webhook.
Errors, limits, concurrency, and state
- Read failure paths, not just the success path. Check what is logged, returned to the caller, retried, or rolled back.
- Set explicit limits for request size, decompression, recursion, pagination, file count, and execution time.
- Examine races around balances, permissions, idempotency keys, token use, and job retries.
- Compare state transitions with the domain rules: can an operation be repeated, skipped, reordered, or completed after cancellation?
- Ensure error messages do not disclose secrets, tokens, internal paths, or another user’s data.
Inspect security-critical decisions and configuration
Cryptography
Do not approve a cryptographic call because its function name sounds familiar. Verify the approved algorithm and mode, key size and source, nonce or IV generation and uniqueness, authentication of ciphertext, rotation and storage, and behavior when verification fails. Check that secrets are not embedded in source, test fixtures, images, logs, or generated configuration. A “temporary” fallback, disabled certificate check, or deterministic test key can become production behavior when generated code is copied forward.
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Secrets, identity, and deployment
- Review environment-variable names, secret-manager references, IAM roles, service-account scopes, and credential lifetime.
- Inspect container, infrastructure-as-code, and deployment manifests for public exposure, permissive network rules, debug mode, writable filesystems, and missing sandboxing.
- Check CI/CD jobs for untrusted pull-request code running with production credentials or write access to release artifacts.
- Confirm that security-sensitive defaults are explicit and that a missing configuration value causes a safe failure.
AISVS 1.0 Appendix C recommends a higher threshold—such as two-person review or security-team sign-off—when AI changes security-critical files. Apply that principle to authentication, authorization, cryptography, secret management, deployment policy, and agent permissions.
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Agent instructions and tool integrations
For agentic coding systems, inspect instruction files, hooks, MCP or other tool integrations, command and network permissions, and workflow edits. Ask what the agent could read, execute, upload, or modify during generation. A rule that tells an agent to bypass a check, a hook that runs generated commands automatically, or a tool credential with broad write access can create a vulnerability even when the final function appears harmless.
Verify every new dependency before accepting it
Generated code can invent a plausible package name or select a similarly named package from the wrong ecosystem. Before installation or merge, verify all of the following:
- Search the intended registry and confirm that the exact package exists for the required language and platform.
- Check that its API and version actually match the code’s use; do not substitute a package merely because its name is close.
- Review provenance, maintainers, release history, licensing, known advisories, and whether the project is maintained.
- Inspect transitive dependencies and install scripts for unexpected network, filesystem, or credential access.
- Prefer an internal registry proxy, allowlist, lockfile review, and package-age or provenance controls where your organization supports them.
- Run dependency and software-composition analysis after the lockfile changes, then review alerts in context.
Never execute a generated install command just because it looks conventional. A newly registered name can be controlled by an attacker and can compromise a developer workstation or build runner before the application is even tested.
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Generated tests are code, not an independent oracle. Compare the test diff with the previous suite and look specifically for deleted cases, weakened assertions, broad snapshots, newly introduced mocks, and tests that merely reproduce the implementation’s assumptions.
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Signs that tests are giving false confidence
- A mock replaces the database, authorization service, cryptographic library, queue, or network behavior that matters in production.
- An assertion checks that a function returned without throwing but does not verify the result, side effect, identity, or persisted state.
- Fixtures use only valid, tiny, single-user, single-threaded, or already-authenticated inputs.
- The test calls internal helpers directly and never exercises the public boundary where validation and authorization should occur.
- A snapshot or golden file was updated to accept a changed result without a documented behavior decision.
Add cases the generator was unlikely to supply
Independently write or review tests for invalid and missing fields, malformed and oversized payloads, expired or revoked credentials, cross-user and cross-tenant access, duplicate requests, concurrent updates, timeouts, partial failures, retries, and rollback. For cryptographic code, test wrong keys, altered ciphertext, nonce misuse protections, and failure handling. For parsers and authorization logic, AISVS recommends differential fuzzing or property-based testing where critical behavior warrants it.
Review changes to the tests themselves in the same pull request. A passing suite is useful evidence about the cases it covers; OWASP cautions that pass rate alone is not security confidence.
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Run the project’s normal quality and security gates on the final diff, not only on the generated snippet. A useful pull-request sequence is:
- Build and unit/integration tests in a clean, reproducible environment.
- Static analysis and semantic queries for data flow, injection, unsafe APIs, and error handling.
- Software-composition and dependency analysis, including lockfile and transitive packages.
- Secret scanning across source, history where appropriate, fixtures, and generated artifacts.
- Infrastructure-as-code and configuration scanning when deployment, containers, IAM, or network policy changed.
- Runtime or dynamic testing for exposed endpoints and critical workflows.
- Manual contextual review of authorization, business rules, and the tool workflow.
| Check type | Good at finding | What it does not establish |
|---|---|---|
| Static or semantic analysis | Known unsafe patterns, tainted data flows, and some API misuse. | That the business operation is allowed or that the intended behavior is correct. |
| Dependency and secret scanning | Known vulnerable components, leaked credentials, and risky package changes. | That an unlisted package is trustworthy or that a secret is used safely. |
| Dynamic tests | Observed failures, integration regressions, and runtime behavior under supplied inputs. | Unexercised paths, rare races, and requirements absent from the tests. |
| Human contextual review | Authorization intent, domain invariants, hidden assumptions, and workflow risk. | Exhaustive coverage of every code path without supporting automation. |
GitHub documentation describes CodeQL as a semantic code-analysis engine and Copilot Autofix as a generator of proposed fixes for CodeQL alerts. Such features can help locate or remediate findings; they are not proof that the complete change is correct. Set merge gates according to your severity policy and investigate critical findings before approval.
Check what the assistant saw and what it could do
Review the context sent to the provider or model. It may include proprietary algorithms, personal data, credentials in terminal output, architecture diagrams, customer records, or files outside the intended task. Configure exclusions for secrets, generated artifacts, and sensitive directories where supported, and verify that logs and transcripts follow your retention and access rules.
For agents, apply least privilege: sandbox the workspace, restrict command and network access, use short-lived credentials, separate read and write permissions, and require confirmation for package installation, deployment, data export, or destructive operations. Record which tools and permissions were active for a high-risk change so a later reviewer can reconstruct the conditions under which code was produced.
Make a human accountable for the merge
Every AI-assisted change needs a named human owner. That person is responsible for correctness, security, maintenance, and explaining the behavior to future engineers. The owner should identify the tool involvement where team policy requires it, summarize material assumptions, confirm which checks ran, and explicitly approve the merge.
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A practical approval checklist
- Intent: The issue, contract, design, and invariants match the diff.
- Scope: All changed source, tests, dependencies, workflows, instruction files, hooks, and manifests were included.
- Boundaries: Inputs, outputs, authentication, authorization, tenants, and service identities were traced end to end.
- Safety: Validation precedes dangerous use; queries are parameterized; dynamic execution and deserialization are justified and constrained.
- Resilience: Errors, limits, retries, concurrency, idempotency, and state transitions behave as intended.
- Secrets and crypto: Approved primitives, key handling, failure behavior, and secret storage were verified.
- Dependencies: Package identity, provenance, versions, lockfiles, and transitive risk were checked before installation.
- Tests: Assertions are meaningful; mocks do not bypass the risk; negative, boundary, adversarial, and security-critical cases exist.
- Automation: Tests, static analysis, dependency and secret scans, and IaC checks ran on the final change, with findings resolved or formally accepted.
- Workflow: Provider context, agent permissions, network access, credentials, and generated commands were appropriate.
- Accountability: A human owner can explain the change and has approved it.
How the guidance fits together
OWASP’s Secure Code Review guidance supplies the risk-based review and data-flow principles; its Secure Coding with AI Cheat Sheet emphasizes human ownership, test skepticism, and the risks of agent instructions and tools. OWASP AISVS 1.0 Appendix C adds expectations for AI-generated code, including stronger review for security-critical files and differential or property-based testing for critical validation and authorization logic. NIST SP 800-218A, finalized on July 26, 2024, supplements SSDF 1.1 with practices and tasks for developing AI models across the software life cycle; it is a development framework, not a measured defect-rate claim. Together these documents support a process in which automation narrows the search and accountable engineers make the final judgment.
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