AI-generated code can pass functional tests and still introduce security flaws. To catch them, inspect how untrusted data reaches interpreters, verify authorization on sensitive operations, scan for exposed secrets, check suggested dependencies, and review what an agent can change. These are five useful failure patterns—not a measured ranking, and not claims that every AI coding tool produces every flaw.
1. Injection-prone data handling and unsafe output
Look for places where user-controlled or otherwise untrusted values cross into an interpreter: a database query, a browser-rendered page, a shell command, or another execution context. OWASP’s DevSecOps guidance uses string-concatenated SQL and eval() as examples of insecure code generation. OWASP also warns that unsanitized model output can lead to cross-site scripting (XSS), and that AI-generated code can introduce SQL injection.
- Database queries: Use parameterized queries or the framework’s safe query API rather than building SQL by concatenating values.
- HTML output: Apply contextual output encoding or framework-safe rendering so untrusted text is treated as data, not markup or script.
- Shell and evaluation: Prefer APIs that pass arguments as data; avoid evaluating strings or assembling commands from untrusted input.
- Trust boundaries: Validate input where it enters the system, but do not treat validation as a substitute for context-appropriate encoding or parameterization.
OWASP’s DevSecOps Guideline notes: “Models are trained on the full breadth of public code — which includes decades of insecure patterns.”
2. Authentication without authorization
A route may correctly establish who a user is and still let that user read or change a record they are not entitled to access. OWASP identifies missing authorization checks on sensitive endpoints as an insecure generation example.
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Review sensitive routes and the data-access paths they call. Confirm that each operation checks both whether the user may perform the action and whether they may perform it on the particular object requested. Do not assume that a login check, a hidden button, or a client-side restriction protects the underlying endpoint.
3. Weak, hardcoded, or exposed secrets
Inspect generated changes and configuration for passwords, API tokens, private keys, and other credentials. Check not only what the assistant writes, but also what project context it can read and send. OWASP cautions that an assistant may transmit broader project context than the active file, and that .gitignore does not stop an AI tool from reading files on disk.
Rank #2
- Keep secrets in environment variables or a dedicated secret store, rather than project files likely to be included in assistant context.
- Exclude sensitive paths from the assistant’s context where the tool supports that control; verify the tool’s actual access boundaries.
- Run secret scanning on proposed changes and repositories, and rotate any credential that has been exposed.
OWASP’s AI-assisted development guidance discusses project context and package verification, while its AI Security and Privacy Guide covers risks from AI tools and their access.
4. Hallucinated or vulnerable dependencies
A suggested package name or version is not proof that the dependency exists, is the intended project, or is safe to use. Models can suggest nonexistent packages and versions that are outdated or vulnerable.
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- Review the selected version for known vulnerabilities and confirm it is appropriate for the project.
- Run software composition or dependency analysis in continuous integration (CI), so later disclosures can be detected against the versions you actually ship.
OWASP’s IDE and AI-Assisted Development Security guidance advises: “Verify every AI-suggested package exists on the public registry before installing.” A model’s suggestion is not a substitute for checking current package and vulnerability information.
5. Unsafe agent, tool, or build changes
An agent can encounter hostile instructions in issue text, pull requests, repository files, fetched pages, or tool descriptions. It may also change files that control CI, package installation, builds, tests, containers, or deployment. Treat both the inputs and the resulting changes as security-sensitive.
Rank #4
- Limit the agent’s context, connected tools, filesystem access, network access, and privileges to what the task requires.
- Sandbox code execution where possible, and treat external repository and pull-request content as untrusted input rather than trusted instructions.
- Review changes to install, build, test, and deployment workflows especially carefully; require explicit human approval for changes with elevated impact.
- After the agent processes external content, inspect its actions and unexpected file or configuration changes.
OWASP’s AI Security and Privacy Guide and AI Security Verification Standard (AISVS) provide guidance for managing these risks. Weak cryptography is another documented insecure code-generation example; the five patterns here are practical inspection groupings, not an exhaustive list.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to check AI-generated code before it ships
Use layered checks: constrain what the assistant can access, review the changes it proposes, run automated analysis on every relevant pull request, and require an accountable human decision before merge. OWASP AISVS says: “Catch the vulnerabilities AI output introduces. Fix them before the code reaches a merge or a deployment.”
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- Keep secrets outside files the assistant may read, and confirm which project files and context the tool can access or transmit.
- Restrict agent permissions, connected tools, filesystem and network access; sandbox execution when available.
During review: inspect the diff, not the label
- Trace trust boundaries and inspect query construction, rendering, command execution, validation, and error paths.
- Check authentication and authorization logic at sensitive endpoints and for individual records and actions.
- Review configuration, dependency changes, secrets, and files that run during install, build, test, or deployment.
- Treat a working demo and passing functional tests as evidence of functionality—not proof of security.
At the pull request: automate checks and enforce policy
Run security checks on every pull request that contains AI-generated code. OWASP AISVS lists static application security testing (SAST), interactive application security testing (IAST), dynamic application security testing (DAST), secret scanning, infrastructure-as-code scanning, and software composition analysis. Choose checks suited to the application and run dependency scanning in CI.
Automation should support, not replace, qualified review. OWASP AISVS calls for a human reviewer other than the person who requested generation; the AI agent itself does not count as that reviewer. It also recommends blocking merges for critical automated findings under the organization’s policy. See the OWASP AISVS guidance for these verification controls.
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