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Do not trust an AI-generated change because it compiles, passes its tests, or clears one scanner. Check it with the same security process used for human-written code: understand the intended change and trust boundaries, inspect the complete diff, examine sensitive data flows and controls, verify every dependency, run source/dependency/secret/configuration checks, and add security tests that the generator did not write. A qualified human must understand the evidence and approve the merge or release.
The most effective review is risk-based. Spend extra time on authentication and authorization, input parsing, deserialization, cryptography, uploads, new integrations, dependency changes, and CI/CD or deployment edits. Automated tools find known patterns and records; they do not prove that business logic is safe in context.
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What is the safest way to review AI-generated code?
Use a layered review in a fixed order, starting with context and the full change set, then tracing data and authorization, checking supply-chain changes, running automated analysis, and testing failure cases independently. Treat AI authorship as a reason to verify scope and assumptions, not as a different security standard. The person approving the change remains responsible for its behavior.
Use a risk-based depth of review
A small copy or layout change may need a proportionate check, while code that handles credentials, personal data, payments, files, network requests, or deployment permissions deserves a deeper review. Identify the assets at risk and the trust boundaries crossed before deciding how much evidence is enough.
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Separate evidence from proof
A clean static-analysis result means that the configured rules found no recognized issue. A dependency audit covers the records in its advisory data, and a passing test demonstrates only the behavior that test exercises. Combine these signals with human reasoning about the complete flow.
How do I establish context before reading the diff?
Write down what the change is supposed to do, which users and systems interact with it, what data and assets it can reach, and which existing controls should remain in force. For a pull request, concentrate on the incremental change and its effect on established architecture; for a new application or major release, examine the architecture, entry points, and dependency model more broadly.
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Define the intended behavior
- State the feature or defect being addressed and the permitted scope of the change.
- Identify critical assets such as accounts, tokens, personal data, financial records, source code, and production credentials.
- Map trust boundaries between browsers, mobile clients, APIs, background jobs, databases, queues, third-party services, build systems, and production.
- List security controls that must still apply, including authentication, authorization, validation, rate limits, encryption, logging, and secret management.
This context lets you spot a change that is functionally plausible but bypasses a control elsewhere in the system.
Which files must I inspect in an AI-authored change?
Start with the complete diff and the complete changed-file list, not only the function named in the task. Agentic coding tools can modify files outside the requested scope, and an unexpected file can be more dangerous than the visible feature code.
Review the whole change set
- Application source, generated code, migrations, and tests.
- Dependency manifests and lockfiles, including transitive version changes.
- CI workflows, build scripts, package lifecycle hooks, Dockerfiles, deployment manifests, and infrastructure configuration.
- Environment templates, scripts that execute shell commands, and files that alter network access or permissions.
- Persistent AI or agent instruction files, repository policies, and documentation that may change how future agents operate.
Build and delivery files are production code: they can run with signing keys, cloud credentials, or deployment privileges. Every unexpected edit needs an explanation, an owner, and a review at the same level as an application-code change.
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Check for scope expansion
Compare the implementation with the issue or approved design. Look for new endpoints, background jobs, outbound requests, file-system access, shell execution, telemetry destinations, feature flags, or permission changes that were not part of the stated goal. If the change cannot be explained in ordinary design terms, pause the merge and clarify it.
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Follow untrusted input from its entry point to every sink, and follow credentials and authorization decisions through the entire request. Security review should cover architecture, entry points, authentication, authorization, data flows, business logic, cryptography, error handling, and configuration, rather than stopping when the code compiles.
Validate at every relevant boundary
- Input parsing: Confirm type, length, encoding, range, and allowed values before processing. Reject malformed and unexpected structures instead of silently coercing them.
- Database access: Ensure queries use parameterization or a vetted query API. Check that filters cannot be removed by user-controlled fragments.
- Files and paths: Constrain upload size and type, normalize paths safely, prevent traversal outside the intended directory, and avoid executing uploaded content.
- URLs and network calls: Validate schemes, hosts, redirects, and response handling. Pay particular attention when a server fetches a URL supplied by a user.
- Deserialization: Use a safe format and an allow-list of permitted types. Do not deserialize attacker-controlled objects with features that can invoke code.
- Output handling: Encode data for its actual context, such as HTML, JavaScript, SQL, shell, headers, or logs. Escaping for one context is not automatically safe for another.
Verify authorization, not just login
Authentication establishes who a caller is; authorization must establish what that caller may do. Check authorization on the server for every sensitive route, function, and object, including direct-object references and background operations. Do not rely only on a client-side control, a hidden button, or the fact that a request carries a valid session.
Inspect secrets, sessions, and cryptography
- Confirm that tokens, passwords, API keys, and personal data are not embedded in source, test fixtures, URLs, or verbose logs.
- Check session creation, expiration, rotation, cookie flags, replay handling, and behavior after privilege changes.
- Use established cryptographic libraries and approved algorithms; review key generation, storage, rotation, and failure behavior.
- Ensure error responses reveal enough for diagnosis without exposing stack traces, credentials, internal paths, or sensitive records.
Examine business-logic and failure paths
Attackers often exploit a valid sequence of individually ordinary operations. Check replay, race conditions, limits, state transitions, partial failures, retries, and rollbacks. Review what happens when a downstream service is unavailable or returns malformed data; a secure default should not become an accidental allow.
How should I verify dependencies suggested by an AI tool?
Validate every new or changed package before installation. Model suggestions can be outdated, incorrect, abandoned, or a lookalike for the intended package.
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Confirm identity and provenance
- Check the exact package name in the intended registry and compare its documented purpose with the required capability.
- Inspect the maintainers, release history, source location, license, and provenance using your normal dependency-review process.
- Review install and lifecycle scripts for shell execution, network access, file changes, or credential collection.
- Prefer a maintained package already accepted by the project when it provides the needed function; avoid adding a large dependency for a trivial task.
- Pin versions according to project policy and commit the lockfile change for review.
Audit known risk and coverage limits
Run the repository’s software-composition analysis or dependency-audit tool against direct and transitive packages, and compare versions with current vulnerability advisories. Triage findings through the normal severity and remediation process. Advisory databases have coverage limits, so a clean result does not establish that a package is harmless; monitor relevant ecosystem advisories when the risk warrants it.
Which automated security checks should run?
Run the checks configured for the repository on the exact commit under review. Each addresses a different failure class, so omitting one leaves a predictable blind spot.
| Check | What it can reveal | What it cannot establish | Reviewer action |
|---|---|---|---|
| Source analysis (SAST) | Recognized insecure patterns in application code and some data flows. | That authorization or business rules are correct in this application’s context. | Investigate findings and manually trace high-risk paths. |
| Software-composition analysis (SCA) | Known vulnerabilities and policy issues in direct and transitive dependencies. | Unknown flaws, malicious intent, or safety outside the database’s coverage. | Verify package identity, version, provenance, and remediation. |
| Secret scanning | Credential-like strings and known secret formats committed to the repository. | Secrets with unfamiliar formats, secrets exposed through logs or services, or whether a detected value is still active. | Revoke exposed credentials, investigate use, and remove them safely. |
| Infrastructure and configuration scanning | Risky cloud, container, CI, deployment, and permissions settings recognized by the rules. | Whether the resulting architecture meets all organizational controls. | Review privilege, network exposure, and deployment behavior in context. |
| Security-focused tests | Whether specified invalid, unauthorized, and failure cases produce the intended result. | Cases that were never written or assertions that merely mirror the implementation. | Add adversarial cases and have an independent reviewer inspect the assertions. |
OWASP guidance says AI-generated code should not be exempt from scanning or testing. Configure failures to block a merge where policy requires it, but treat tool output as findings to verify and triage rather than as a security certificate.
How do I design independent security tests?
Do not rely only on tests generated alongside the implementation. Add tests from the security property you require, preferably by someone who did not write the production code.
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- Malformed, oversized, empty, duplicated, and boundary-value inputs.
- Missing, expired, revoked, or incorrectly scoped credentials.
- Attempts to read or modify another user’s object by changing an identifier.
- Unexpected content types, encodings, redirects, file names, and serialized values.
- Repeated requests, concurrent updates, retries, partial failures, and downstream timeouts.
- Error paths that could disclose data or leave a transaction in an unsafe state.
Assert the security property
A test that expects a particular status code can still pass while the wrong user receives the response. State who should be allowed or denied, which data must remain unchanged, and what side effects must not occur. Ensure tests are not simply copied from the implementation’s branches, because that reproduces the same mistaken assumption.
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How should I review the AI agent’s operating context?
If an agent read an issue, pull request, web page, documentation, or dependency text, treat that content as untrusted input. Prompt-injection text can instruct an agent to expose secrets, weaken controls, or make unrelated edits.
Limit permissions and retained access
- Give the agent only the repository, branches, commands, and network access required for the task.
- Keep production credentials, signing keys, and broadly scoped cloud tokens out of its environment.
- Use isolated credentials for development and revoke them when the task ends.
- Review changes to persistent instruction files because they can influence later agent actions.
- Keep an audit trail linking the agent run, resulting commit, human reviewer, and approval decision.
Inspect what the agent actually did, not only the final diff: commands run, packages installed, files read, network destinations contacted, and secrets encountered can reveal exposure that source review misses.
How do I triage findings and approve the change?
Resolve or explicitly disposition every finding under your organization’s severity policy before merge or release. A dismissal should record why the issue is not applicable, who accepted the risk, and any compensating control or follow-up date.
Escalate high-impact changes
Require appropriately qualified review for authentication, authorization, cryptography, CI/CD, deployment permissions, secret handling, and new trust boundaries. The reviewer should be able to explain what the code does, which controls protect it, what scans and tests were considered, and why remaining risk is acceptable.
Use a human approval gate
The UK Home Office Engineering Guidance and Standards states: "AI-assisted code MUST meet the same security expectations as human-written code." Make that standard operational by requiring a named human to understand and approve the change; an AI-generated review is supporting input, not accountable approval.
What does GitHub’s 2026 agent validation cover?
On June 9, 2026, GitHub announced security validation for third-party coding agents that checks generated code with CodeQL, checks new dependencies against the GitHub Advisory Database, and scans for secrets. Availability, settings, and coverage are product-specific and may change, so verify how the feature is configured in your repository.
Use that validation as an additional automated layer. It does not replace reviewing the complete diff, examining business logic and authorization, testing adversarial behavior, or obtaining qualified human approval.
What should be on the reviewer’s final checklist?
Before merging or releasing, answer each question with evidence from the diff, scans, tests, and review notes:
- Did I inspect every changed file, including lockfiles, workflows, build and deployment files, generated code, tests, and agent instruction files?
- Do I understand every unexpected change and every new trust boundary?
- Are inputs constrained, database operations parameterized, outputs encoded for their context, and deserialization restricted?
- Is authorization enforced server-side for each sensitive action and object?
- Are credentials, secrets, personal data, and diagnostic errors protected?
- Are cryptographic libraries, algorithms, and key-handling practices approved?
- Did the change add packages, install behavior, network access, shell execution, or privileged build/deploy behavior?
- Did source, dependency, secret, and relevant infrastructure/configuration checks run on this commit, and were findings triaged?
- Do independent tests cover invalid input, unauthorized access, boundary values, and failure handling?
- Has a qualified human documented understanding and approved the change?
If any answer is unknown, the right outcome is to pause, gather the missing evidence, or reduce the change—not to infer safety from a green pipeline.
Frequently Asked Questions
Can I merge AI-generated code when all scanners are green?
Only if the accountable reviewer also understands the changed behavior and has checked the relevant security properties. Green results indicate that configured rules found no recognized issue; they do not cover unknown vulnerabilities, incorrect business authorization, or untested failure paths.
Should I ask another AI model to review the first model’s code?
A second model can suggest review questions and spot obvious patterns, but it is not independent proof. Treat its output like any other automated aid and have a qualified human verify the claims against the code, tests, and system context.
What should I do if an AI tool changed a lockfile or CI workflow unexpectedly?
Stop the merge and identify why the file changed, which commands or packages caused it, and what permissions the change introduces. Revert unrelated edits, then review the remaining dependency or workflow change as production-sensitive code.
Do scripts and infrastructure files need the same review as application code?
Yes when they can execute commands, alter permissions, access networks, build artifacts, or deploy systems. Their smaller size does not reduce the impact of credentials and privileges available at build or delivery time.
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