A comprehensive guide to developing securely with AI coding assistants. Learn how to leverage AI productivity while maintaining security standards.
AI models can generate code with security vulnerabilities including XSS, SQL injection, hardcoded secrets, and insecure configurations. Never accept code without reviewing it.
Accept all suggestions without reading them. Trust that AI knows best.
Review each suggestion for vulnerabilities, validate inputs, check for hardcoded secrets.
AI coding tools with terminal access (Cursor, Windsurf, Claude Code) can execute system commands. Require explicit approval for every command to prevent prompt injection attacks.
Automated security scanners can catch vulnerabilities that humans miss. Run scans on every PR that contains AI-generated code.
Indirect prompt injection attacks hide malicious instructions in websites, repositories, and documents. Be careful what you ask AI to analyze.
Run AI coding tools in containers or VMs without access to production credentials, SSH keys, or sensitive files.
AI frequently generates incomplete or insecure authentication logic. Always manually verify auth code.
Studies show AI-generated code has similar vulnerability rates to human code, but AI may produce vulnerabilities at scale faster. The key difference is human developers can reason about security implications while AI cannot.
No. AI tools significantly boost productivity. The key is using them responsibly: review code, enable command approval, use security scanning, and understand the risks.
Create policies for: mandatory code review of AI suggestions, required security scanning in CI/CD, approved AI tools list, and training on prompt injection risks.
Input validation issues (XSS, SQL injection) and missing authorization checks are the most common. AI often generates 'happy path' code without defensive programming.
Find vulnerabilities in your codebase before they reach production. Works with code from any AI tool.
Scan Your App FreeLast updated: January 2025