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

Security engineer David Seagal has released VibeGuard, a free open-source security linter designed specifically for AI-generated code. In a real production codebase audit, the tool scanned 533 files and found three critical vulnerabilities, including SQL injection and hardcoded secrets, all missed by existing security pipelines. VibeGuard provides exact one-line fixes and supports Python, JavaScript, and TypeScript.

AI coding tools write fast. GitHub Copilot. Cursor. ChatGPT. Claude. Gemini. Every single one of them is now inside production codebases at companies worldwide. And every single one of them produces the same security vulnerabilities repeatedly. Not because they are bad tools. Because they were trained on code that had security mistakes in it — and they learned those patterns too. A developer handed me their production codebase to audit. I ran my new tool on it. Here is what came back: 533 files scanned. 3 critical security vulnerabilities. All produced by AI coding tools. All missed by their existing security pipeline. All fixed with exact one-line commands. The tool is called VibeGuard. VibeGuard is a free open-source security linter built specifically for AI-generated code. Not a generic SAST tool. Not another Bandit wrapper. Every rule was written by studying actual AI-generated code and cataloguing the exact vulnerability patterns these tools produce. Here is what it catches: → SQL injection via f-strings Copilot writes f"SELECT FROM users WHERE id = {user id}" Looks clean. Works perfectly. Also lets any attacker dump your entire database. → Hardcoded API keys and passwords AI tools write secrets directly into source code because that is what training examples looked like. → JWT without algorithm verification ChatGPT generates jwt.decode token, secret Missing the algorithms parameter. Vulnerable to the alg:none bypass attack. → Command injection via shell=True subprocess.run cmd, shell=True everywhere. User input reaches the shell. Game over. → MD5 for password hashing Still appearing in AI-generated auth code in 2026. Crackable in seconds. → DEBUG=True shipped to production Every AI tool sets this by default. Exposes your entire stack to anyone who triggers an error. Every finding includes the exact working code to fix the problem. Not a description. Not a link to documentation. The actual replacement code. 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. Zero configuration. Grades your code A to F. Grade F. 3 critical findings. In files I thought were secure. That is the point. AI tools are fast. They are powerful. They will not slow down. But they need a security layer between what they generate and what ships to production. VibeGuard is that layer. MIT licensed. Zero cloud. Zero tracking. Everything runs locally on your machine. Contributions welcome — especially rules for Go, Rust, Java and infrastructure as code. 🔗 github.com/zeroFhacker/vibeguard Run it on your codebase before the next deployment. It takes 30 seconds. It is free. It might save you from a very bad day. Built by David Seagal — security engineer and open source builder at github.com/zeroFhacker