I Built a Security Linter for AI-Generated Code, Here Is What I Found in a Real Production Codebase

AI coding tools write fast. GitHub Copilot. Cursor. ChatGPT. Claude. Gemini. And every single one of them produces the same Not because they are bad tools. A developer handed me their production codebase I ran my new tool on it. Here is what came back: 533 files scanned. The tool is called VibeGuard. VibeGuard is a free open-source security linter Not a generic SAST tool. Not another Bandit wrapper. Every rule was written by studying actual Here is what it catches: → SQL injection via f-strings → Hardcoded API keys and passwords → JWT without algorithm verification → Command injection via shell=True → MD5 for password hashing → DEBUG=True shipped to production Every finding includes the exact working code Not a description. Copy. Paste. Fixed. git clone https://github.com/zeroFhacker/vibeguard cd vibeguard python3 -m venv venv source venv/bin/activate pip install -r requirements.txt PYTHONPATH=. python -m vibeguard.cli scan --path ./your-project Works on Python, JavaScript and TypeScript. Grade F. 3 critical findings. That is the point. AI tools are fast. They are powerful. But they need a security layer between VibeGuard is that layer. MIT licensed. Zero cloud. Zero tracking. Contributions welcome — especially rules for 🔗 github.com/zeroFhacker/vibeguard Run it on your codebase before the next deployment. It takes 30 seconds. Built by David Seagal — security engineer and

I Built a Security Linter for AI-Generated Code, Here Is What I Found in a Real Production Codebase

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I Built a Security Linter for AI-Generated Code, Here Is What I Found in a Real Production Codebase

Key Takeaways

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