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What changes when AI writes or modifies code?
The development workflow speeds up, but security responsibilities do not move to the assistant. Coding tools can produce implementation code, suggest packages, edit tests, and use repository or external content as context. That means a change can introduce risk through more than its final code: a stale dependency suggestion, an instruction hidden in content the agent reads, weakened tests, or sensitive information included in context can all matter.
OWASP’s Secure Coding with AI Cheat Sheet addresses these workflow risks. The practical response is to treat AI as a contributor whose output needs ordinary controls plus careful attention to its access, context, and test changes—not as a reason to exempt code from review or as proof of a higher vulnerability rate.
What should a team verify before merging?
Use layered checks because each one answers a different question. Human review evaluates intent and design; automated tools look for particular classes of defects; dependency auditing checks known risks in selected components and versions. None substitutes for the others, and ownership records make decisions traceable rather than detecting vulnerabilities themselves.
#1 Best Overall
| Control | What it can help assess | When to use it | What it does not establish |
|---|---|---|---|
| Qualified human review | Whether the implementation matches the intended behavior, design, and security assumptions, and whether automated findings are relevant. | Before merge, with a qualified reviewer; use elevated review for security-critical changes. | Review alone does not guarantee that every vulnerability is found. |
| SAST | Potential security weaknesses identified through static application security testing. | Run on relevant pull requests as part of the organization’s automated checks. | A clean result does not prove that the application is secure or that runtime behavior is safe. |
| DAST and IAST | Potential weaknesses identified through dynamic or interactive application security testing. | Include them in the applicable pull-request or test workflow. | Neither replaces review of design, dependencies, secrets, or infrastructure configuration. |
| Secret scanning | Potentially exposed credentials and other secrets. | Run against relevant changes and repositories. | It does not assess application logic or whether credentials are stored and accessed safely in every context. |
| Infrastructure-as-code scanning | Potential security issues in infrastructure configuration represented as code. | Run when the change includes relevant infrastructure definitions. | It does not establish the security of application code or deployed behavior by itself. |
| Software composition analysis (SCA) and dependency auditing | Known risks associated with selected components and versions. | Audit proposed dependencies and versions, and run checks in CI. | It does not establish the correctness of application logic or guarantee that every dependency risk is known. |
| Independent adversarial tests | Whether behavior holds under deliberately difficult or security-relevant inputs and conditions. | Review test changes and add cases designed independently of the agent’s implementation. | A passing suite is not independent assurance when the same agent generated both the code and the tests. |
| Human owner and audit trail | Who is accountable for a change and which tool and model contributed. | Record before merge and retain with the change. | Accountability records do not themselves detect defects. |
How do you verify AI-generated code before merging it?
- Set the rules before work begins. Define which AI tools are approved, what data and repository context they may receive, which permissions they need, and what kinds of changes require elevated review. Make the policy apply to assistants and agents as well as human-authored changes.
- Assign a human owner and review the change. Identify a developer responsible for the result before it enters the merge queue. Have a qualified reviewer assess the implementation against its intended behavior and security assumptions; require elevated expertise for security-critical code.
- Inspect dependency additions and version changes. Review why each proposed package is needed and whether its selected version is appropriate. Run the ecosystem’s normal audit tools, cross-check versions against vulnerability databases, and make CI fail on known vulnerabilities according to the organization’s documented policy. OWASP recommends applying these checks whether code was written by a person or generated by AI.
- Run layered security checks on relevant pull requests. OWASP AISVS Appendix C identifies SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and SCA among the automated checks for AI-generated code. Configure the checks that fit the change and the team’s environment; one scan class is not a stand-in for the others.
- Review test changes and add independent cases. Look for deleted tests, weakened assertions, or mocks that remove the behavior under test. Add human-designed cases for malformed inputs, expired credentials, boundary conditions, and concurrency where they are relevant. For security-critical functions, have a qualified person define the expected behavior and tests.
- Apply a documented merge decision. Block merges for critical scan findings according to the organization’s severity threshold and exception process. OWASP AISVS describes this kind of control; its stated threshold is an example, not a universal severity policy. Any exception should follow an authorized, written process.
- Record approval and contribution details. Keep an audit record identifying the approving developer and the AI tool and model version involved. Continue to maintain and monitor the merged code through the normal software lifecycle.
Why is a passing AI-authored test suite not enough?
Tests provide evidence only to the extent that their design meaningfully challenges the implementation. If an agent creates both the code and its tests, both may encode the same mistaken assumption. The agent can also make a suite pass by deleting tests, weakening assertions, or mocking away the behavior that needs to be checked.
OWASP’s Secure Coding with AI Cheat Sheet states: “A passing test suite generated by the same agent that produced the code provides no independent assurance.” Treat that as a reason to inspect the test diff and add independently designed adversarial cases, not as a reason to discard useful AI-authored tests.
How should teams handle agent context and permissions?
Review what files and terminal context the assistant can send to its provider, and configure exclusions for secrets or sensitive directories where the tool allows it. Git ignore settings should not be mistaken for controls on what an AI tool can read. Keep credentials in environment variables, a vault, or an encrypted secret store rather than in project-tree files that could be exposed through the agent’s context.
Apply least-privilege thinking to the tool’s access: provide only the repository, files, and actions needed for the task. Also consider content the agent reads, not just instructions written by the development team; indirect prompt injection can arrive through that content. Tool capabilities and data-handling terms can change, so check the current documentation for the specific tool and model in use.
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How do NIST SSDF and OWASP AISVS fit together?
NIST SP 800-218A is the Secure Software Development Framework community profile for generative AI and dual-use foundation models. NIST describes the broader SSDF as fundamental secure-development practices that can be added to software life-cycle models. It is a process framework, not a product certification or proof that a particular application is secure.
OWASP AISVS 1.0 is an open, community-driven, vendor-neutral catalogue of testable security requirements for AI-enabled systems across their life cycle. OWASP reports that the version released in June 2026 contains 191 requirements across 12 chapters and three appendices, with each requirement carrying verification level 1, 2, or 3. Appendix C specifically addresses AI for code generation.
The two are complementary: use SSDF to structure secure-development practices across the lifecycle, and AISVS to identify testable verification requirements. AISVS’s Appendix C offers a concrete reference for code-generation workflows, while neither framework certifies that a given application is secure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can the available evidence say about AI-written code risk?
The sources cited here support process controls and verification requirements; they do not establish an empirical vulnerability rate for AI-written application code. A team should not infer that AI-generated code is inherently insecure—or safe—from the presence of a model, a passing test suite, or a clean scan. The sound decision is to apply the secure-development baseline to every change and make verification independent, layered, and accountable.
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