AI Security Linter for Vibe-Coded Applications That Catches Vulnerabilities Before Deployment
VibeSec scans GitHub repos for vulnerabilities. As AI-generated code ('vibe coding') grows, so do security vulnerabilities in that code. AI generates functional code that passes tests but introduces SQL injection, XSS, insecure API keys, and missing auth checks that junior developers who vibe-code don't catch. A security linter purpose-built for AI-generated code patterns would catch the specific vulnerability types AI assistants consistently introduce.
Problem Statement
A developer asks Cursor to build an API endpoint. The generated code works perfectly but has SQL injection vulnerability because the AI used string concatenation instead of parameterized queries. The developer, who relied on AI because they lack deep backend experience, doesn't recognize the vulnerability. This pattern repeats across every AI-generated codebase: functional code with predictable security gaps.
The Idea
A security linter purpose-built for AI-generated code that detects the specific vulnerability patterns AI coding assistants consistently introduce, SQL injection in generated queries, missing auth middleware, hardcoded secrets, and insecure defaults.
Why Now
AI-generated code now represents 30-50% of new code at many companies. AI assistants optimize for functionality, not security, they generate code that works but contains SQL injection, XSS, missing authentication, and insecure defaults. Traditional static analysis tools flag thousands of issues; developers need a focused tool that catches the specific patterns AI consistently gets wrong.
Target User
Solo developers and small teams shipping AI-generated code without security review processes
Target Market
Development teams using AI coding assistants for 30%+ of their code output
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