Security Risks of AI-Generated Code

Generated code can be syntactically correct and still be vulnerable. It may omit authorization checks, mishandle untrusted input, expose secrets, select an unsafe dependency or use cryptography incorrectly. These are not unique to AI: they are familiar software-security risks, but fast code generation can increase the amount of code that needs review. A working demo is not a security assessment. Start by defining the threat model and trust boundaries. What inputs can an attacker control? What data or actions can the code access? What permissions should the feature have? Inspect the implementation for input validation, output encoding, authentication, authorization, error handling, logging, secrets and dependency use. Validate generated package names against official registries and review maintenance and advisories. Never assume a library exists or that a suggested version is safe. Run the same controls used for human-written code: code review, unit and integration tests, static analysis, dependency and secret scanning, and dynamic testing where appropriate. Tests need to include adversarial and boundary cases. Execute unfamiliar scripts or generated code in a sandbox with minimal permissions, no production credentials and controlled network access. Require explicit approval before agents can alter infrastructure, deploy, delete data or run privileged commands. OWASP’s current Top 10 for LLM applications includes improper output handling, where applications fail to validate or sanitize model output before using it downstream. That risk concerns how an application handles generated text or code; it is separate from a coding assistant proposing a vulnerable program. In both cases, enforce security checks in deterministic software and review processes rather than relying on the model to police itself.