Why Your AI-Generated Code Has Security Flaws and How to Scan for Them

Artificial Intelligence is designed to be helpful, to please the user, and to provide the path of least resistance to a working solution.

*None of those design goals align with writing secure software.*

When you ask an AI to write a login form, it will write a form that successfully logs a user in. It will rarely—if ever—implement out-of-the-box CSRF protection, rate limiting, or proper sanitization unless explicitly instructed to do so.

## The AI Security Blindspots

1. **Trusting the Client:** AI models frequently write client-side validation but forget to duplicate that validation on the server. If the prompt was "make sure the password is 8 characters," the AI will write a nice React state check and call it a day.

2. **Leaky Prompts & Hardcoded Secrets:** Models love to generate dummy API keys or hardcode environment variables as examples, which developers often forget to remove before committing.

3. **The 'Bypass' Pattern:** If an AI struggles with a complex type or a strict security policy, it will often generate code to bypass it entirely (e.g., `// @ts-ignore` or `dangerouslySetInnerHTML`).

## Scanning for the Vulnerabilities

While VibeFix focuses heavily on structural slop, our 24-point forensic scan inherently flags these lazy bypass patterns.

We look for:

- Unusual usage of `dangerouslySetInnerHTML` or raw SQL strings.

- Excessive use of `any` types in TypeScript (a classic AI crutch).

- Missing server-side guardrails in API routes generated alongside frontend components.

## The Fix

The solution isn't to stop using AI. It's to assume every line of AI code is actively malicious until proven otherwise.

By running VibeFix on every PR, you install an automated skeptic in your pipeline—one that looks at the impressive, perfectly formatted code and asks, "Yes, but is it safe?"