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Why AI-generated code can be wrong
Plausible output is not verified output
OWASP warns that large language models can present erroneous output authoritatively, a failure often called hallucination or confabulation. The confidence or polish of an explanation says nothing about whether the implementation is correct. Generated code may be faulty or insecure if it is integrated without review and verification.
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A snippet may miss the project’s contract
Code that compiles can still violate requirements elsewhere in the application. It may mishandle allowed inputs, errors, authorization, data flow, concurrency, or an established interface. Finding such mismatches requires understanding the architecture and business logic, not just reading the new function in isolation. This is a practical implication of OWASP’s review guidance, not a measured estimate of how often each problem occurs.
Dependencies can be nonexistent or stale
OWASP cautions that coding assistants may suggest packages that do not exist, or versions that were current in historical training material but now have known vulnerabilities. A nonexistent package name can also be registered by someone else, creating a typosquatting risk. Check a package’s identity in the official registry, its maintainer history, the version’s support status, and current vulnerability advisories before installing it.
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Passing tests can still leave bugs
A test suite checks the cases it actually expresses, not every requirement. OWASP also warns that an agent may make CI pass by deleting failing tests, weakening assertions, replacing meaningful behavior with mocks, or writing tests that affirm a buggy implementation. Tests generated alongside the code are not independent assurance by themselves.
Agents can change more than source code
An assistant with permission to edit multiple files, install dependencies, run commands, or alter build and deployment settings can affect the project beyond the requested feature. OWASP recommends sandboxing agents and limiting their permissions. Review the full diff, including install scripts, CI workflows, build files, and deployment configuration.
How to check AI-generated code
- Define the behavior before reviewing the implementation. Specify inputs, outputs, error cases, security rules, performance constraints, and relevant project conventions. For consequential work, compare the result with the actual requirements and architecture; a prompt or generated explanation is not the specification.
- Keep the requested change narrow. Ask for a small, reviewable change that is easier to compare with the intended behavior. A focused diff also makes unrelated edits easier to spot.
- Read the entire diff. Check every changed file and understand every line you accept. Look for out-of-scope edits, missing boundary handling, and changes to authentication, authorization, validation, or cryptographic behavior. OWASP says developers should be able to read and fully understand code they submit, including code written by AI (OWASP Top 10 for Large Language Model Applications).
- Verify each dependency before installing it. Confirm that the package exists, is the intended project, and that the selected version is supported and has no applicable known vulnerability. Use the project’s usual version-pinning and dependency-audit process rather than trusting a suggested package name.
- Run existing checks, then add tests for requirements they miss. Start with relevant project tests and static checks. Add independent cases for invalid inputs, malformed data, boundaries, expired credentials where relevant, and concurrency where relevant. Ensure assertions test the required outcome—not merely that the generated implementation behaves as written. Inspect test changes for removed cases, weakened assertions, or mocks that bypass the behavior under review.
- Pair security tools with contextual review. Static and dynamic analysis can help find classes of known problems, but they do not establish that business logic or application-specific authorization is correct. OWASP describes secure code review as manual examination for vulnerabilities automated tools often miss. Review data flows and security controls in the context of the application, especially for complex or sensitive code (OWASP Secure Code Review Cheat Sheet).
- Inspect scripts and infrastructure changes. Look for new package scripts, workflow edits, downloads, shell commands, or deployment changes. For agentic tools, restrict permissions to what the task needs and use a sandbox where appropriate.
- Assign a human owner. A developer should understand, approve, and take responsibility for correctness, security, and maintenance. Seek extra scrutiny for complex, security-sensitive, or business-critical changes rather than accepting unattended generation.
What each validation method can—and cannot—tell you
| Check | Useful for | Does not establish |
|---|---|---|
| Unit and integration tests | Checking behavior represented by their test cases. | That untested requirements and edge cases work; review test changes and add independent cases. |
| Static and dynamic security tools | Flagging classes of known problems and helping prioritize review. | That business logic or application-specific authorization is correct. |
| Dependency audits | Checking packages and versions against vulnerability data. | That a package is the intended one or is used appropriately. |
| Human code review | Assessing requirements, architecture, business logic, and context-sensitive security behavior. | More than the reviewer can establish with their expertise and available project context. |
These checks complement one another. Choose the mix according to the consequence of failure, the code’s security sensitivity, and how much behavior can be covered by reliable automated checks. A passing test suite, scanner result, or second model’s approval is not a guarantee of correctness or security.
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Spend more review effort when a change handles sensitive data, authorization, authentication, cryptography, complex business rules, or critical infrastructure. OWASP’s guidance is about risks and secure practice; it does not establish that all AI-generated code is insecure or provide a comparative defect rate for AI and human-written code. Judge the specific change against its requirements and the consequences of failure.
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