AI-generated code can pass functional tests and still contain serious security flaws. To catch them, inspect how untrusted data is handled, verify authorization and secrets, check every proposed dependency, and treat agent access and build changes as security-sensitive. These are five useful failure patterns—not a measured ranking of the most common AI coding errors, and not flaws that every assistant produces.
Why AI-generated code needs a security review
AI assistants can reproduce patterns found in public code, including insecure ones. The OWASP DevSecOps Guideline puts it plainly: “Models are trained on the full breadth of public code — which includes decades of insecure patterns.” A demo that works or a passing functional test suite does not establish that the code is secure.
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The five checks below group documented risks for practical review; they do not claim a universal order of frequency. Weak cryptography is another example of insecure code generation identified by OWASP, so this list is not exhaustive.
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Look for untrusted values crossing into an interpreter or being rendered as active content. A generated change may concatenate user-controlled input into a SQL statement, pass it to a shell, or render it as HTML without the protections required by that context. OWASP’s AI-assisted development guidance gives string-concatenated SQL and eval() as insecure code-generation examples; its LLM risk guidance on improper output handling describes risks including XSS and SQL injection.
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- SQL: Use parameterized queries or the framework’s safe query API. Do not build a query by interpolating untrusted strings.
- HTML: Use contextual output encoding or safe templating defaults. Do not assume model-produced text is safe to render as markup.
- Shell and other interpreters: Prefer APIs that pass arguments as data instead of constructing executable command strings. Validate inputs at trust boundaries.
Review the actual language and framework behavior: “sanitize the input” is not a universal substitute for using the correct safe API and encoding for each output context.
2. Missing authorization checks
Authentication establishes who a caller is; authorization determines whether that caller may perform a particular action on a particular resource. A route can require a logged-in user and still expose another user’s record if it accepts an object ID without checking ownership or permission.
For each security-sensitive endpoint and data access path, verify both object-level access (is this caller entitled to this record?) and operation-level access (may this caller perform this action?). OWASP lists missing authorization checks on sensitive endpoints as an insecure code-generation example in its IDE and AI-assisted development guidance. Trace the check through the real request path rather than relying on a UI control to hide an action.
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3. Weak, hardcoded, or exposed secrets
Inspect generated diffs and configuration for passwords, API tokens, private keys, and credentials embedded in code or test fixtures. Also inspect what the assistant can read and send: the current file may not be the only project context it receives.
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- Keep secrets in environment variables or a dedicated secret store, not in project files available to the assistant.
- Exclude sensitive paths from assistant context where the tool supports that control, and verify the exclusion actually applies.
- Run secret scanning on changes and investigate findings before merge.
Do not treat .gitignore as an AI context boundary. OWASP warns that AI tools may read files from the filesystem even when Git ignores them, and that assistants can send broader project context than the file currently open. See the OWASP guidance on IDE and AI-assisted development.
4. Hallucinated or vulnerable dependencies
A suggested package name or version is not verified just because it appears in plausible code. A model may recommend a package that does not exist in the intended registry, confuse it with a similarly named project, or select a version that has since become vulnerable.
- Before installing, verify that the package exists in the registry you intend to use and check its identity and maintenance history. OWASP’s guidance says to “Verify every AI-suggested package exists on the public registry before installing.”
- Review the selected version and its known vulnerability status; do not assume the model knows about newly disclosed issues.
- Run software composition analysis or equivalent dependency scanning in CI so later vulnerability disclosures can be detected against the versions you use.
OWASP discusses these risks in its AI-assisted development guidance and verification recommendations.
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5. Unsafe agent, tool, or build changes
Code agents can act on more than the prompt typed by a developer. Issues, pull requests, repository files, fetched pages, and tool descriptions can contain hostile instructions. An agent may also change CI workflows, package scripts, containers, or deployment configuration—files that can execute code or alter what reaches production.
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- Limit the agent’s context, connected tools, filesystem access, network access, and privileges to what the task needs.
- Sandbox execution, especially when processing untrusted repository or external content.
- Review changes to install, build, test, and deploy workflows as security-sensitive code, not incidental configuration.
- Require explicit human review for changes with elevated impact, and inspect logs and diffs for unexpected actions after the agent handles external content.
OWASP’s IDE and AI-assisted development guidance discusses risks from broad context and untrusted inputs; its AI Security Verification Standard supports controls around agent use and review.
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, inspect the change at pull request, run automated security tests, and require a qualified human decision before merge. OWASP’s AI Security Verification Standard (AISVS), Appendix C says: “Catch the vulnerabilities AI output introduces. Fix them before the code reaches a merge or a deployment.”
Before generation: reduce exposure and authority
- Review which files, conversation history, and other project context the assistant can read or transmit.
- Keep secrets out of accessible project files; do not rely on Git ignore rules to keep them away from an AI tool.
- For agents, restrict tools, permissions, filesystem and network access, and execution environment to the task’s needs.
At pull request: inspect the change and run checks
- Review the diff. Trace trust boundaries, data handling, authorization logic, error paths, secrets, and security-sensitive configuration. Ask what changed and what a caller can now control.
- Verify dependencies before installation. Check registry identity, maintenance history, and selected versions; include dependency scanning in CI.
- Run automated checks on every pull request containing AI-generated code. AISVS identifies SAST, IAST, DAST, secret scanning, infrastructure-as-code scanning, and software composition analysis as relevant verification methods. Select checks that fit the application and run them as part of the normal review workflow.
- Review the agent’s actions. Treat repository and pull-request content as untrusted input; examine its changes and logs, especially when it had tools or privileges beyond reading code.
- Get an independent, qualified human review. AISVS calls for a reviewer other than the person who requested generation; the AI agent does not count as that reviewer. Apply the organization’s merge policy to critical automated findings, including blocking merge when policy requires it.
These controls cover different failure surfaces: context restrictions reduce what can leak or be manipulated; code review examines logic; scanners detect classes of code, secret, infrastructure, and dependency problems; and merge policy determines whether a finding stops release. No single passing check establishes that every risk has been addressed.
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