Why AI-generated code has security vulnerabilities and how to build securely with AI assistance.
LLMs are trained on billions of lines of code from GitHub, Stack Overflow, and other sources. Much of this code has security vulnerabilities, and the model learns these patterns.
AI models are trained to generate code that works, not code that's secure. Security is often at odds with simplicity, and AI favors simpler solutions.
AI doesn't know your threat model, compliance requirements, or what data you're handling. It generates generic code without security context.
AI may claim code is secure or suggest security measures that don't actually work. It can be confidently wrong about security.
AI often generates API routes and pages without proper authentication checks
Users can access or modify other users' data due to missing ownership verification
User input concatenated into database queries instead of using parameterization
API keys, passwords, and credentials written directly in code
User input rendered without sanitization, allowing script injection
Resources accessed by guessable IDs without authorization checks
Explicitly ask for secure implementations: 'Implement this with proper authentication, authorization, and input validation'
Treat all AI-generated code as untrusted. Review with security focus before accepting.
Integrate SAST/DAST tools to catch vulnerabilities AI introduces automatically.
Use checklists to verify auth, authorization, input validation, and output encoding.
Establish patterns for your project that AI should follow (via context/rules files).
Test the application's security before launch, not just the code quality.
Based on internal research analyzing vibe-coded applications
Automated security scanning catches the vulnerabilities that AI introduces. Scan your codebase in minutes to find what needs fixing.
Free Security ScanNo, AI-generated code is not inherently secure. Studies have shown that code from AI assistants has similar or higher vulnerability rates compared to human-written code. AI is trained on code that contains vulnerabilities, optimized for functionality over security, and lacks understanding of your specific security requirements.
Use AI cautiously for security-sensitive code. AI can help scaffold implementations faster, but every security-relevant suggestion needs careful human review. For critical security functions (authentication, authorization, encryption), consider using well-tested libraries rather than AI-generated implementations.
1) Use security-focused prompts that explicitly request secure implementations, 2) Provide context about your security requirements in rules files, 3) Ask the AI to explain security considerations in its suggestions, 4) Always review and test security-critical code regardless of source.
Yes, AI can be helpful for security review. You can ask AI to review code for vulnerabilities, explain potential security issues, or suggest fixes. However, AI review shouldn't replace proper security testing—it's a complement to, not a replacement for, security tools and expert review.
The most common AI-generated vulnerabilities are: missing authentication/authorization checks, hardcoded credentials, SQL/NoSQL injection from improper query construction, XSS from unsanitized output, and insecure direct object references. These are the same vulnerabilities common in human code—AI just generates them faster.
Last updated: January 16, 2026