{"slug": "i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real", "title": "I Built a Security Linter for AI-Generated Code, Here Is What I Found in a Real Production Codebase", "summary": "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.", "body_md": "AI coding tools write fast.\n\nGitHub Copilot. Cursor. ChatGPT. Claude. Gemini.\n\nEvery single one of them is now inside production\n\ncodebases at companies worldwide.\n\nAnd every single one of them produces the same\n\nsecurity vulnerabilities repeatedly.\n\nNot because they are bad tools.\n\nBecause they were trained on code that had\n\nsecurity mistakes in it — and they learned\n\nthose patterns too.\n\nA developer handed me their production codebase\n\nto audit.\n\nI ran my new tool on it.\n\nHere is what came back:\n\n533 files scanned.\n\n3 critical security vulnerabilities.\n\nAll produced by AI coding tools.\n\nAll missed by their existing security pipeline.\n\nAll fixed with exact one-line commands.\n\nThe tool is called VibeGuard.\n\nVibeGuard is a free open-source security linter\n\nbuilt specifically for AI-generated code.\n\nNot a generic SAST tool. Not another Bandit wrapper.\n\nEvery rule was written by studying actual\n\nAI-generated code and cataloguing the exact\n\nvulnerability patterns these tools produce.\n\nHere is what it catches:\n\n→ SQL injection via f-strings\n\nCopilot writes f\"SELECT * FROM users WHERE id = {user_id}\"\n\nLooks clean. Works perfectly.\n\nAlso lets any attacker dump your entire database.\n\n→ Hardcoded API keys and passwords\n\nAI tools write secrets directly into source code\n\nbecause that is what training examples looked like.\n\n→ JWT without algorithm verification\n\nChatGPT generates jwt.decode(token, secret)\n\nMissing the algorithms parameter.\n\nVulnerable to the alg:none bypass attack.\n\n→ Command injection via shell=True\n\nsubprocess.run(cmd, shell=True) everywhere.\n\nUser input reaches the shell. Game over.\n\n→ MD5 for password hashing\n\nStill appearing in AI-generated auth code in 2026.\n\nCrackable in seconds.\n\n→ DEBUG=True shipped to production\n\nEvery AI tool sets this by default.\n\nExposes your entire stack to anyone who triggers an error.\n\nEvery finding includes the exact working code\n\nto fix the problem.\n\nNot a description.\n\nNot a link to documentation.\n\nThe actual replacement code.\n\nCopy. Paste. Fixed.\n\n```\ngit clone https://github.com/zeroFhacker/vibeguard\ncd vibeguard\npython3 -m venv venv\nsource venv/bin/activate\npip install -r requirements.txt\nPYTHONPATH=. python -m vibeguard.cli scan --path ./your-project\n```\n\nWorks on Python, JavaScript and TypeScript.\n\nZero configuration.\n\nGrades your code A to F.\n\nGrade F. 3 critical findings.\n\nIn files I thought were secure.\n\nThat is the point.\n\nAI tools are fast. They are powerful.\n\nThey will not slow down.\n\nBut they need a security layer between\n\nwhat they generate and what ships to production.\n\nVibeGuard is that layer.\n\nMIT licensed. Zero cloud. Zero tracking.\n\nEverything runs locally on your machine.\n\nContributions welcome — especially rules for\n\nGo, Rust, Java and infrastructure as code.\n\n🔗 github.com/zeroFhacker/vibeguard\n\nRun it on your codebase before the next deployment.\n\nIt takes 30 seconds.\n\nIt is free.\n\nIt might save you from a very bad day.\n\n*Built by David Seagal — security engineer and\nopen source builder at github.com/zeroFhacker*", "url": "https://wpnews.pro/news/i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real", "canonical_source": "https://dev.to/david_seagal_77ff697cd0f6/i-built-a-security-linter-for-ai-generated-codehere-is-what-i-found-in-a-real-production-codebase-38hp", "published_at": "2026-08-30 13:11:36+00:00", "updated_at": "2026-08-30 13:53:35.929385+00:00", "lang": "en", "topics": ["ai-safety", "developer-tools", "ai-products"], "entities": ["David Seagal", "VibeGuard", "GitHub Copilot", "Cursor", "ChatGPT", "Claude", "Gemini"], "alternates": {"html": "https://wpnews.pro/news/i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real", "markdown": "https://wpnews.pro/news/i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real.md", "text": "https://wpnews.pro/news/i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real.txt", "jsonld": "https://wpnews.pro/news/i-built-a-security-linter-for-ai-generated-code-here-is-what-i-found-in-a-real.jsonld"}}