Problem

AI coding tools (Copilot, Claude, Cursor) repeatedly introduce specific vulnerability patterns:

  • Veracode: 45% of AI-generated code samples fail security tests
  • 6.4% of GitHub Copilot repos leak at least one secret (40% higher than non-AI repos)
  • AI code security findings hit 10,000+ per month by June 2025 (10x increase)
  • Existing SAST scanners (Snyk, Semgrep) don’t recognize patterns unique to LLM-generated code

Pain Intensity: 9/10 - Measurable, costly, and worsening problem

Market

  • Primary Market: Engineering teams using AI coding assistants in production
  • Segment: Teams running security scans in CI/CD pipelines
  • TAM: Application Security Testing (AST) market $3.73B → $8.37B (2033), 9.4% CAGR
  • SAM: AI security startups raised $6.34B in 2025 (3x YoY)
  • M&A Signal: ServiceNow spent $11.6B on security acquisitions in 2025 alone

Solution

AI Code CVE Pattern Detector - AI-generated code specific vulnerability scanner with AI tool attribution tracking

Core Features

  1. AI Code Pattern Detection: Dedicated rules for LLM-recurring vulnerability patterns (hardcoded secrets, incomplete auth, etc.)
  2. AI Tool Attribution: Track which AI assistant (Copilot vs Claude vs Cursor) introduced vulnerabilities
  3. Context-Aware Fixes: Fix suggestions that consider the original AI prompt context
  4. CI/CD Integration: Native GitHub Action and GitLab CI integration

Usage Scenario

# Add to CI/CD pipeline
- name: AI Code CVE Scan
  uses: ai-cve-detect/action@v1
  with:
    scan-mode: ai-patterns
    track-attribution: true

# Example output
┌──────────────────────────────────────────────────┐
│ AI Code CVE Scan Report                          │
├──────────────────────────────────────────────────┤
│ 🔴 HIGH: Hardcoded API key in auth.ts:42         │
│    Source: GitHub Copilot (87% confidence)        │
│    Pattern: AI-HARDCODED-SECRET-001               │
│    Fix: Use environment variable injection        │
│                                                   │
│ 🟡 MED: Incomplete input validation in api.ts:18 │
│    Source: Claude (72% confidence)                │
│    Pattern: AI-MISSING-VALIDATION-003             │
│    Fix: Add Zod schema validation                 │
└──────────────────────────────────────────────────┘

Competition

CompetitorPriceWeakness
Snyk$52/dev/moNo AI tool attribution tracking, generic SAST
Semgrep$40/dev/moHeavy rule-writing burden, not AI-specific
ZeroPathUndisclosedAI-native but no attribution tracking
Veracode$15,000+/yrEnterprise-only, inaccessible to small teams
Aikido SecurityFree tier availableGeneral-purpose, not AI code pattern specific

Competition Intensity: High - Snyk/Semgrep dominate, can add features quickly Differentiation: AI tool attribution tracking + AI-generated code specific pattern library

MVP Development

  • MVP Timeline: 9 weeks
  • Full Version: 8 months
  • Tech Complexity: Medium-High
  • Stack: Node.js/Python (scan engine), React (dashboard), Docker, GitHub Actions

MVP Scope

  1. 20 AI code pattern rules for JavaScript/TypeScript
  2. GitHub Action CI integration (PR comments)
  3. Basic attribution tracking (git blame + AI comment patterns)
  4. Text-based fix suggestions

Revenue Model

  • Model: Subscription (per-repo)
  • Pricing:
    • Starter: $49/repo/mo (up to 5 repos, basic rules)
    • Team: $199/mo (20 repos, attribution dashboard, team analytics)
    • Enterprise: Custom (SSO, audit logs, API access)
  • Expected MRR (6 months): $3,000-8,000
  • Expected MRR (12 months): $12,000-30,000

Risk

TypeLevelMitigation
TechnicalMediumAI attribution accuracy → combine git blame + code pattern heuristics
MarketHighSnyk/Semgrep can add AI attribution quickly → need first-mover in 12-18 months
ExecutionHighRule library maintenance + false positive management is ongoing burden

Recommendation

Score: 85/100 ⭐⭐⭐⭐

  1. Largest market ($8.37B) + explosive investment ($6.34B in 2025)
  2. 45% AI code security failure rate = clear, measurable problem
  3. AI tool attribution is unique — no existing tool offers this
  4. Security tools have compliance-driven purchasing = high retention

Risk Factors

  1. Security domain expertise gap is an execution risk
  2. Snyk/Semgrep can add features faster than a solo developer
  3. False positive management creates ongoing operational burden

First Actions

  1. Write 10 AI code-specific pattern rules in Semgrep format as PoC
  2. Collect vulnerability pattern data from Copilot-generated code in open source repos
  3. Deploy MVP as GitHub Action with PR comment integration

This idea is inspired by the discussion “AI ships your code but can’t fix the CVEs it creates,” proposing a differentiated approach through AI-generated code specific vulnerability patterns and AI tool attribution tracking.