Treat code from an unrestricted AI model as untrusted until a qualified human has reviewed the actual changes and the project’s security checks have passed. “Unrestricted” describes the agent’s permissions and autonomy—not a guarantee that its code is vulnerable. Reduce risk on two fronts: inspect and test the code it produces, and limit what the agent can read, execute, and access.
Separate code-generation risk from agent-runtime risk
There are two distinct security questions. First, does the generated code introduce a weakness, such as missing authorization checks, unsafe input handling, or a vulnerable dependency? Second, could the agent itself misuse its permissions—for example, by reading sensitive files, running an unsafe command, or acting on hostile instructions in an issue or pull request?
Controls for one do not replace controls for the other. A secure sandbox cannot prove the code is safe, and clean scan results do not make broad agent permissions appropriate. The cited guidance does not establish a universal vulnerability rate for AI-generated code or show that it is inherently less secure in every case; the practical response is to apply secure-development gates consistently and contain the agent.
Set boundaries before the agent starts
Decide what the tool may see and do
Write a usage policy that names approved tools and use cases, prohibited operations, and data that must not be sent to third-party services. Do not put secrets or sensitive files in the prompt or workspace context. An assistant may collect more project context than the file currently visible in its interface, and a .gitignore entry does not prevent a tool from reading a local file. Use supported context exclusions for secret files; where policy requires it, use an approved enterprise or self-hosted arrangement.
#1 Best Overall
Limit execution, network access, and credentials
For agentic tools that can act on a repository or run commands, start with least privilege. Use an isolated workspace, sandbox, development container, or VM; allow only necessary commands and tools; restrict filesystem access; and block unnecessary outbound network connections. Give credentials only the permissions and lifetime needed for the task. Keep production credentials, SSH keys, and organization-wide secrets out of the agent’s reach, and avoid automatic acceptance of actions in unfamiliar or untrusted repositories. OWASP’s Secure Coding with AI Cheat Sheet discusses these safeguards.
Review the change, not just the explanation
Require an independent, qualified human engineer to review the actual diff. OWASP’s AISVS control AC.4.1 says the reviewer should not be the same identity that requested generation and that the AI agent itself does not count as the reviewer. The accountable developer should approve and own the change; an AI-generated review or a passing test suite is not a substitute.
Keep the changes attributable and narrow enough to review. Look for unexpected files, unexplained scope changes, weakened or deleted tests, newly added dependencies, network calls, shell execution, exposed secrets, and altered validation or authorization behavior. If the agent cannot explain why a change is necessary, treat that as a reason to investigate, not as proof that it is safe.
Give sensitive code and build paths extra scrutiny
Require heightened review when changes touch authentication, authorization, cryptography, identity and access management (IAM), CI/CD, deployment, or sandbox and network policy. Also inspect files that may execute automatically or in privileged contexts, including package installation scripts, CI workflows, Dockerfiles, build configuration, and deployment manifests. Verify new dependencies and review their provenance and behavior. OWASP recommends pinning third-party GitHub Actions to immutable commit SHAs rather than mutable tags.
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Run layered security checks on every applicable change
Run the repository’s normal security pipeline on AI-assisted changes just as you would on human-written changes. The relevant checks depend on the application and build system:
- Static application security testing (SAST): identify suspicious code patterns without running the application.
- Software composition analysis (SCA): examine dependencies for known vulnerabilities and policy issues.
- Secret scanning: look for credentials or tokens accidentally added to code or configuration.
- Infrastructure-as-code scanning: check infrastructure and deployment definitions for insecure settings.
- Dynamic and interactive analysis (DAST and IAST): test a running application or observe its behavior where the project and pipeline support those methods.
Set explicit severity thresholds and make critical findings merge-blocking. OWASP AISVS Appendix C gives CVSS >= 9.0 as an example threshold for a critical issue, or an organization may use its equivalent severity policy. Any bypass should require a written, human-approved exception with an accountable owner; a scanner finding should not disappear merely because it is inconvenient.
Rank #3
NIST IR 8397 offers a broader verification menu, including threat modeling, static analysis, checks for hardcoded secrets, black-box and structural testing, fuzzing, web application scanners where applicable, and review of included code. It is general software verification guidance, not a study of AI-generated code. NIST SP 800-218A is a companion profile to the SSDF focused on generative-AI and dual-use foundation-model development, so it should not be read as though every practice directly governs arbitrary code produced by an assistant.
Test security behaviors scanners may miss
Write adversarial tests independently of the generation step. A green test run is evidence only for the behaviors the tests actually assert; an AI-generated test suite or its pass rate alone does not establish security. Include cases suited to the changed code, such as:
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- Expired credentials and attempts to access another user’s or role’s resources.
- Concurrent requests that may expose race conditions or inconsistent authorization.
- Unsafe deserialization and other risky parsing behavior.
For security-critical input validation, authorization, and deserialization, OWASP AISVS AC.4.5 specifically calls for differential fuzzing or property-based tests. Choose the technique that fits the component, and investigate failures rather than treating the test command’s exit status as the whole result.
Keep untrusted pull-request content away from powerful agents
Issue text, pull-request descriptions, comments, and diffs may be attacker-controlled when an agent reads them. Constrain or sanitize that context, isolate CI agents, and grant only the minimum job-specific permissions. A review bot should not receive deploy keys or secrets it does not need, and an agent processing untrusted contributions should not have broad write privileges or access to CI secrets.
Maintain audit records that connect the suggestion to the human who approved it, the resulting commit, and deployment where feasible. Record the tool or model version when available. Keep a practical way to revoke credentials or pause the agent if its behavior is suspect.
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If a security check finds a vulnerability, stop the merge or deployment under the project’s policy, record and triage the finding, remediate the underlying issue, and rerun the relevant checks. If credentials may have been exposed, revoke or rotate them and investigate which systems the agent could reach and whether it made outbound connections. Follow the organization’s incident-response plan for the details.
Best Value
When choosing scanners or containment approaches, compare them against the repository and workflow rather than assuming one tool is universally best:
- Language and framework coverage, and which check types are supported: static, dependency, secret, infrastructure-as-code, dynamic, fuzz, or property-based testing.
- Integration with the existing CI flow, including whether a finding can block a merge.
- False-positive handling and the human triage effort required.
- Data handling and the amount of project context exposed to a service.
- Agent permissions, filesystem and network boundaries, and credential scope.
- Auditability: whether reviewers can trace a change from generation through approval and deployment.
OWASP AISVS Appendix C contains AI-assisted coding controls; OWASP’s coding and DevSecOps guidance is maintained and may change, so check the current versions when adopting a control. NIST IR 8397 and SP 800-218A are useful framework references, but neither supplies a product ranking or a universal scanner configuration.
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