Audit AI-generated code as production code: a qualified human must review the complete change, verify dependencies, run security checks suited to the stack, and approve it against explicit release gates. Neither a prompt asking the AI to review its own work, passing tests, nor one clean scanner run proves that a change is secure.
Who is responsible for approving AI-generated code?
The human owner of the change remains accountable for it. OWASP’s Secure Coding with AI Cheat Sheet puts it plainly: “AI-generated code must have a human owner.” OWASP’s AI Security Verification Standard (AISVS) Appendix C calls for qualified human review; an AI agent does not qualify as that reviewer. It also recommends separating the reviewer from the person who requested the code where practical.
Record which parts of the change were generated or modified with AI, the affected services and security-sensitive files, the tool or model if known, and the human who will approve the patch. Keep the ordinary change record and review trail. AI attribution is context for review, not a reason to relax secure-development controls.
How should you inspect the change itself?
Compare the complete diff with the intended design
Review the full patch, not only the lines attributed to AI. Compare it with the task and the system’s intended architecture. Look for unrelated edits, weakened checks, unsafe defaults, exposed debug behavior, unexpected network or filesystem access, and missing error handling. Trace data from entry points to sensitive operations and ask what trust boundary changed and what new assumptions the code introduces.
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Check security-sensitive paths affected by the patch
- Identity and access: authentication, authorization, role checks, and tenant or data isolation.
- Untrusted data: input validation, output encoding, and construction of SQL queries or operating-system commands.
- Sensitive information: secret handling, cryptographic use, and whether errors or logs expose credentials or personal data.
- Configuration and behavior: unsafe defaults, debug settings, new network or filesystem access, and error handling.
These are reviewer checks to apply where the change touches them, not a claim that every patch has the same risk or needs the same checklist.
How do you audit dependencies suggested by AI?
Review supply-chain changes alongside source code. A generated patch can introduce a package with a lookalike or nonexistent name, or select an outdated vulnerable version. For every new or changed dependency:
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- Confirm the package name, source, and intended purpose; do not install a suggestion based on its name alone.
- Inspect direct and transitive versions and verify that the lockfile reflects the reviewed versions.
- Run the package ecosystem’s supported dependency-audit process and check relevant advisories, such as those from NVD, GitHub Advisory Database, or OSV.
OWASP’s AI cheat sheet names npm audit, pip audit, govulncheck, and cargo audit as examples of ecosystem auditing tools. Use the process appropriate to your language and project; a dependency audit checks known component issues, not every security flaw in the application.
Which automated security checks belong in the release workflow?
Run applicable checks on the pull request or other release workflow that contains the AI-assisted change. OWASP AISVS lists these verification categories:
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- Interactive application security testing (IAST) and dynamic application security testing (DAST), where appropriate to the system
- Secret scanning
- Infrastructure-as-code scanning
- Software composition analysis (SCA), including dependency analysis
Select checks according to the changed system, languages, deployment configuration, and risk; there is no evidence here for one universal scan set or a scanner that finds every vulnerability. NIST’s recommended minimum standard for code verification notes that static analysis can identify many vulnerabilities and coding-standard violations, but it is one technique rather than a security guarantee. A clean result should be considered alongside human review and other relevant checks.
Do the tests demonstrate safe behavior?
Inspect tests to see whether they assert the security property at issue, not just whether the happy path works. For a changed authorization rule, for example, check that tests exercise denied access as well as allowed access. For input handling, look for relevant malformed or hostile cases. A passing suite is evidence that its assertions passed; it does not establish that the assertions cover the risk.
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OWASP warns against treating AI-generated tests as security evidence by themselves. Review any agent change to existing tests, especially deletions or weakened assertions, and require a clear, human-reviewed justification before accepting it.
How should you review the AI agent’s workflow?
Code is not the only security concern when an agent works with a repository. Issue text, pull-request comments, documentation, logs, package changelogs, and fetched web pages are untrusted input. They may contain instructions that try to redirect the agent, weaken safeguards, or expose data.
- Review unexpected edits and the agent’s actions after it has consumed external or repository content.
- Limit the context and permissions available to the agent to what the task requires.
- Check what source code or other context is sent to a hosted provider, according to your organization’s data-handling rules.
These controls address risks specific to the workflow that produced the patch; they do not replace reviewing the resulting code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should block a merge or release?
Set an explicit severity policy and make the relevant checks enforce it. OWASP AISVS Appendix C gives blocking a pull request on a critical finding as an example, with CVSS >= 9.0 or an organization’s equivalent severity threshold. That is a control example in the standard, not a universal legal requirement or a substitute for local policy.
- Do not allow unresolved findings that meet the organization’s blocking threshold to merge silently.
- Require any bypass to be a written exception approved by an authorized human, with the finding and rationale recorded.
- Apply elevated review to security-critical files when policy calls for it; this may mean a second reviewer or security-team sign-off.
Before release, record the findings and remediation, applicable scan results, accountable human approver, and any approved exception. The reviewer should be able to explain the security-sensitive changes rather than merely confirm that a tool passed.
How should you choose audit tools?
Editor plugins, CI scanners, dependency-audit tools, and manual review serve different parts of the process. Compare them on the dimensions below rather than assuming a named product or a single scan covers the whole patch. OWASP DevSecOps discusses IDE plugins, including examples such as Snyk and Semgrep; those examples are not a ranking or a guarantee of coverage.
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| Option | What to evaluate | Limit to account for |
|---|---|---|
| Editor security plugin | Language and framework coverage; feedback during coding; private-code handling and outbound context. | Findings still need human triage and review; editor feedback alone does not enforce a release gate. |
| Pull-request or CI security scanner | Relevant vulnerability classes; integration with the release workflow; severity policy and enforceable gates; finding quality and triage burden. | Coverage depends on the scanner and configuration; a clean run does not prove the change is secure. |
| Dependency-audit tool | Direct and transitive dependency coverage; lockfile support; advisory sources and freshness; ecosystem compatibility. | Known component advisories do not amount to a complete application-code review. |
| Qualified human review | Understanding of the changed system, trust boundaries, security-sensitive behavior, and the intended architecture; a clear approval trail. | It complements automated checks; it should not be replaced by asking the generating AI to approve its own output. |
OWASP DevSecOps, OWASP AISVS, and NIST’s code-verification guidance support these workflow categories and evaluation dimensions; they do not establish comparative effectiveness for particular vendors.
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