Skip to content

Model Deployment Honeypot: Proactive AI Security Scanner

As AI models become critical infrastructure, they become attack targets. This platform continuously tests your deployed models with adversarial scenarios, prompt injections, data poisoning attempts, and extraction attacks—finding vulnerabilities before hackers do.

App Concept

  • Automated penetration testing platform specifically designed for AI/ML model endpoints
  • Library of 500+ known AI attack patterns (prompt injection, jailbreaks, model extraction, etc.)
  • Continuous monitoring that runs synthetic attacks against your staging and production environments
  • Instant Slack/PagerDuty alerts when vulnerabilities are discovered
  • Remediation playbooks with code examples for fixing each vulnerability type
  • Integration with CI/CD pipelines to block deployments that fail security tests

Core Mechanism

  • Connect your model API endpoints (OpenAI, Anthropic, self-hosted) via secure configuration
  • Platform automatically runs graduated attack scenarios from benign to sophisticated
  • Real-time security dashboard shows vulnerability severity scores and exploit paths
  • Weekly security reports track improvement over time and benchmark against industry standards
  • Gamification: Security score (0-100) with achievements for maintaining high scores
  • Social proof: Anonymous industry security benchmarks show how you compare

Monetization Strategy

  • Free tier: 100 security scans/month for single endpoint
  • Pro tier ($299/mo): Unlimited scans, 5 endpoints, Slack integration
  • Enterprise tier ($1,999/mo): Unlimited endpoints, custom attack scenarios, compliance reports
  • Security audit service: $5,000 one-time comprehensive penetration test by security experts
  • Certification program: $500/year for "AI Security Verified" badge after passing rigorous testing

Viral Growth Angle

  • Public CVE-style database of AI vulnerabilities discovered (with anonymization)
  • Security researchers share novel attack patterns through the platform
  • Annual "State of AI Security" report with shocking statistics drives media coverage
  • Conference talks showing live demonstrations of model exploits
  • Fear-driven sharing: "We found 23 critical vulnerabilities in production" posts on LinkedIn

Existing projects

  • HiddenLayer - AI/ML security platform focused on model protection
  • Robust Intelligence - AI firewall and security testing
  • Arthur AI - Model monitoring with some security features
  • Protect AI - AI/ML security scanner with focus on MLOps infrastructure
  • Garak - Open-source LLM vulnerability scanner
  • PromptArmor - Prompt injection detection and prevention

Evaluation Criteria

  • Emotional Trigger: Limit risk - prevent catastrophic security breaches; be first to catch vulnerabilities before competitors or attackers
  • Idea Quality: Rank: 8/10 - High emotional intensity (fear of security breach) + growing market as AI adoption increases
  • Need Category: Stability & Security Needs (Level 2) - Secure model deployment and predictable performance under adversarial conditions
  • Market Size: $500M+ market - estimated 50K+ companies deploying production AI models, $5K-$50K annual value per company
  • Build Complexity: High - requires deep AI security expertise, attack pattern library, safe sandbox execution, and integration with multiple platforms
  • Time to MVP: 12-16 weeks with AI coding agents (basic attack library + scanning), 24-30 weeks without
  • Key Differentiator: Only platform combining automated adversarial testing, continuous monitoring, and CI/CD integration specifically designed for AI model security (not general application security)