VibeShield
AI-generated code has a 45% vulnerability rate. One-click security scan for vibe-coded apps, explained in plain English.
The Idea
A security scanning tool built specifically for applications created with AI coding tools (Cursor, Bolt, Lovable, Replit Agent, Claude Code). Non-technical founders are shipping production apps without any security review. VibeShield connects to a GitHub repo or accepts a URL, runs targeted scans for the exact vulnerability patterns AI models produce, and returns results in plain English with one-click fix suggestions. Not another developer tool with CVE numbers and severity matrices. A simple red/amber/green dashboard that tells a non-technical founder: "Your login page stores passwords in plain text. Here is the fix. Click to apply." Targets the specific failure modes of AI-generated code: hallucinated package dependencies (slopsquatting), exposed API keys, missing input validation, broken access controls, and insecure defaults.
Why Now
The vibe coding explosion hit critical mass in early 2026. Georgia Tech tracked 6 CVEs from AI-generated code in January, 15 in February, and 35 in March. Veracode found 45% of AI-generated code samples introduce OWASP Top 10 vulnerabilities. AI-assisted commits expose secrets at twice the rate of human code (3.2% vs 1.5%). Twenty percent of AI-generated code references packages that do not exist, creating "slopsquatting" attack vectors. The UK NCSC head publicly called for vibe coding safeguards at RSA Conference 2026. Meanwhile, thousands of non-technical founders are deploying AI-built apps to Vercel and Railway daily with zero security review. The gap between "I can build an app" and "I can build a secure app" has never been wider.
How to Build
Three scan modes: (1) GitHub repo scan via API, pulling source code and running static analysis for known AI vulnerability patterns. (2) URL scan that crawls a live app checking for exposed environment variables, missing security headers, open admin panels, and misconfigured CORS. (3) Code paste for quick checks on individual files. Core detection engine: check package.json/requirements.txt against npm/PyPI for hallucinated packages (the 20% that do not exist). Scan for hardcoded secrets using pattern matching (API keys, database URLs, JWT secrets). Check authentication implementations for common AI mistakes (client-side auth only, missing rate limiting, no CSRF protection). Validate input handling on forms and API routes. Report output: plain English summary, risk score, prioritised fix list with code snippets. Use Claude to generate human-readable explanations and fix suggestions specific to the user's stack. Deploy on Vercel, use Supabase for scan history.
Revenue Model
Free tier: one repo scan per month, limited to 10 files, shows overall risk score only. Pro: £29/month for unlimited scans, full reports, fix suggestions, Slack/email alerts on new vulnerabilities. Team: £99/month adds multiple repos, scheduled weekly scans, compliance export for SOC 2/ISO 27001 evidence. One-time audit: £149 for a comprehensive report with executive summary suitable for investor due diligence. Enterprise: £499/month for agencies building apps for clients who need security sign-off before handover. The per-scan pricing also works: £9 per scan for pay-as-you-go users. Target is the 2.8M+ developers now using AI coding tools, specifically the non-technical segment who cannot interpret traditional security scanner output.
Effort
Two weeks to MVP. Week one: GitHub OAuth integration, static analysis engine for the top five AI vulnerability patterns (hallucinated packages, exposed secrets, missing auth checks, SQL injection, XSS). Plain English report generator using Claude. Stripe checkout. Week two: URL scanner for live apps (security headers, exposed .env files, admin panel detection), scan history dashboard, email alerts. The hardest engineering challenge is reducing false positives since non-technical users will lose trust immediately if the tool cries wolf. Build a confidence scoring system and only surface high-confidence findings in V1. The hallucinated package check is the easiest win and the most novel: cross-reference every import against the actual npm/PyPI registry.
Competitive Landscape
Snyk, SonarQube, Checkmarx, and GitHub Advanced Security all exist but they are built for developers. Their output assumes you know what XSS means, what a CVE is, and how to patch a dependency. None of them specifically target AI-generated code patterns. None explain findings in plain English. None offer one-click fixes. The Unit 42 SHIELD framework (January 2026) provides a governance template but no automated tooling. The gap is clear: security tooling for people who build with AI but are not security engineers. That audience barely existed 18 months ago. Now it is millions of people shipping production code daily.
Verdict
8/10. The timing is exceptional: the vibe coding security crisis is front-page news in tech, CVEs are surging exponentially, the NCSC is publicly calling for solutions, and nobody has built the plain-English security scanner for non-technical builders yet. The market is massive and growing daily. The risk is that GitHub or Vercel ship their own version (Vercel already has some basic checks), but the "explain it to a non-developer" angle is defensible because enterprise security companies have no incentive to dumb down their products. Build this before someone else does.
The vibe coding security crisis is real and accelerating. 45% vulnerability rate in AI code, CVEs surging monthly, and millions of non-technical founders shipping insecure apps. VibeShield fills the gap between enterprise security scanners and the people who actually need them. Plain English, one-click fixes, AI-pattern-specific detection. The timing is now.