How to Secure AI-Generated Code: A Complete Guide

The Rise of AI-Generated Code

AI code assistants have transformed software development. GitHub reports that developers using Copilot write code 55% faster. But with great power comes great responsibility.

Common Security Issues in AI-Generated Code

1. SQL Injection Vulnerabilities

AI models often generate code that concatenates user input directly into SQL queries. This is one of the most common—and dangerous—vulnerabilities.

Bad (AI-generated):

const query = "SELECT * FROM users WHERE id = " + userId;

Good (manually fixed):

const query = "SELECT * FROM users WHERE id = ?";
db.query(query, [userId]);

2. Hardcoded Secrets

AI models have been trained on code that includes hardcoded API keys and passwords. They sometimes reproduce this pattern.

Bad:

const API_KEY = "sk-1234567890abcdef";

Good:

const API_KEY = process.env.API_KEY;

3. Insecure Random Number Generation

For security-sensitive operations, AI often suggests Math.random() instead of cryptographically secure alternatives.

4. Missing Input Validation

AI-generated code frequently lacks proper input validation and sanitization.

How QODRYX Helps

Our AI-aware security scanner understands these patterns and catches them before they reach production:

Pre-commit hooks: Block vulnerable code at the source

AI-specific rules: Detect patterns common in AI-generated code

Auto-fix suggestions: Get immediate remediation guidance

Secret scanning: Find hardcoded credentials automatically

Best Practices

Always review AI-generated code - Don't trust, verify

Use security scanning tools - Automate what you can

Enable pre-commit hooks - Shift security left

Regular dependency updates - Keep your supply chain secure

Conclusion

AI code generation is here to stay. The key is to pair AI productivity with proper security measures. Tools like QODRYX make this possible without slowing down your team.

Start scanning your code for free →