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AI-generated code is not secure by default. Before you merge or release it, verify every suggested dependency, trace untrusted data through sensitive operations, test authorization boundaries, and review the changes—including the agent’s permissions and project configuration. Treat the output like any other code that must meet your application’s security requirements.
Why AI-generated code needs a security review
Code that compiles or passes a happy-path test can still rely on a vulnerable package, mishandle user input, or omit an authorization check. A coding assistant may also act on misleading project content or make risky changes when it has permission to run commands, install packages, read files, or use the network.
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Use your usual language- and environment-specific secure coding practices, and add checks for dependency identity and the AI tool’s workflow. NIST’s Secure Software Development Framework (SSDF) is lifecycle guidance, not a guarantee that generated code is safe. NIST SP 800-218A is the final July 2024 profile for generative AI and dual-use foundation models; it augments SSDF 1.1 and is intended to be used with it. NIST lists SP 800-218 Rev. 1 Version 1.2 as an initial public draft dated December 17, 2025, not as a final revision. NIST SP 800-218A · NIST SSDF revision listing
Fix the most common security problems
1. Verify AI-suggested packages before installing
A model can suggest a package that does not exist, confuse it with a similarly named project, or name a package that an attacker could register under a plausible hallucinated name. Before adding a dependency, confirm its exact registry entry and identity, check its provenance, maintainers, and maintenance history, and ask whether the project needs it at all. Prefer an established, approved package when one meets the need; managed environments can enforce allowlists or installation policies.
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Do not blindly run an installation command suggested by an assistant. Review dependency-file changes and package scripts as code, not as harmless setup details. OWASP’s Secure Coding with AI Cheat Sheet covers hallucinated and typosquatted dependencies.
2. Check dependencies for known vulnerabilities
Even a real package can be unsafe at the suggested version. Models may reflect historical information and miss newer disclosures. Run the audit tool used by your ecosystem and consult a current vulnerability source. Pin the version you select and update it through the project’s normal dependency process. Set CI or merge policy to block vulnerable dependencies according to your severity rules.
OWASP lists npm audit, pip audit, govulncheck, and cargo audit as examples. They are ecosystem-specific examples, not a universal ranking or a substitute for your team’s dependency policy. OWASP’s dependency guidance
3. Protect every interpreter boundary
Trace user-controlled values through the generated code. Check whether they reach SQL queries, shell commands, HTML, templates, file paths, deserializers, or another interpreter without the protection appropriate to that context. Use parameterized queries for database access, safe APIs instead of shell-string construction where possible, and framework-appropriate encoding for output. Validate inputs against the application’s requirements; reject or safely handle invalid values.
Rank #3
Apply the same distrust to prompts, retrieved content, tool results, and model-generated output in AI features. NIST SP 800-218A says inputs and outputs should be logged, analyzed, and validated in model context; problematic values should be sanitized or dropped. It also states: “Encode inputs and outputs to prevent the execution of unauthorized code.” Encoding and parameterization must match the specific interpreter and framework—there is no generic sanitizer that makes every sink safe. NIST SP 800-218A, PW.5.1
4. Check authorization and trust boundaries
Compare the code with explicit application security requirements. Follow the data flow across authentication, authorization, tenant separation, and least-privilege boundaries. A generated endpoint that returns the right result for its developer may still expose another user’s or tenant’s data if it fails to check ownership or permissions.
Rank #4
- Used Book in Good Condition
Write negative tests for unauthorized access and other failure cases, not just tests showing that an authorized request succeeds. Review whether each sensitive operation checks the actor, resource, and required permission at the right point. These are practical checks to apply to your design and requirements; they do not imply a measured rate of defects in AI-generated code. NIST recommends established secure coding practices appropriate to the language and environment. NIST SP 800-218A
5. Constrain the coding agent and its context
Source review does not address every risk in an agent-assisted workflow. Issues, pull requests, READMEs, dependency files, fetched pages, tool responses, and repository instruction files can contain text that influences an agent. Treat that content as untrusted input, especially when the agent can act on instructions by running commands, installing packages, or editing files.
- Run agents in a constrained environment, such as a dev container or ephemeral workspace, where practical.
- Allow only the commands and filesystem access needed for the task.
- Keep secrets, SSH material, cloud credentials, and sensitive directories out of reach; restrict outbound network access when it is not required.
- Review changes to persistent agent instructions, dependencies, build scripts, CI, and deployment configuration.
OWASP discusses indirect prompt injection, tool and MCP risks, sandboxing, and changes to automation in its AI secure-coding guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical review and release workflow
- State the security requirement. Identify the data, trust boundaries, permissions, and sensitive operations the change affects.
- Review the diff. Trace new and changed code, including error handling, configuration, dependency files, build scripts, and automation—not only the main function or endpoint.
- Verify dependencies. Confirm each package’s identity and provenance, decide whether it is necessary, and run the ecosystem’s dependency audit.
- Trace untrusted values. Follow prompts, user input, retrieved material, and tool or model output to interpreters and sensitive operations. Validate, parameterize, encode, or reject values using the method appropriate to each destination.
- Test boundaries and failures. Add tests for unauthenticated and unauthorized requests, cross-tenant access, malformed input, and other relevant negative cases.
- Run review and analysis. Use code review and applicable static or other code analysis, then triage findings and fix them through the normal development workflow. NIST SSDF treats review and analysis as ways to identify vulnerabilities for correction, not proof that none remain. NIST SSDF 1.1
- Check the agent’s footprint. Confirm its permissions were limited to the task and inspect changes to instructions, dependencies, credentials handling, CI, build, and deployment files.
- Make the release decision. Resolve policy-blocking findings and have a human review high-impact changes against the threat model before release.
What a clean scan does—and does not—tell you
Automated analysis can find issues within the tools’ coverage, but a clean result is not proof of security. A scanner may not understand the application’s authorization rules, tenant boundaries, or intended data flow; it may also miss a vulnerable package or flaw outside its coverage. An AI-generated review is not independent assurance either. Use findings to guide investigation, and use requirements, threat-model review, and human judgment for high-impact changes.
The cited OWASP and NIST guidance does not establish a prevalence percentage for vulnerabilities in AI-generated code, nor does it establish a vendor ranking or product-efficacy comparison. Choose review and audit controls based on your languages, frameworks, vulnerability classes, advisory coverage, CI policy needs, and data-handling constraints.
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