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Model Deployment Honeypot: AI Security Testing Platform

Companies deploying LLM features have no idea if they're vulnerable to prompt injection, jailbreaks, or data exfiltration until it's too late. This platform creates realistic decoy AI endpoints that attract and analyze attacks.

App Concept

  • Deploy honeypot AI endpoints that mimic your real LLM services
  • Attracts attackers with intentionally discoverable "vulnerabilities"
  • Captures and analyzes attack patterns: prompt injections, jailbreak attempts, data poisoning
  • Provides early warning system: "This attack pattern will likely hit production soon"
  • Generates defensive patches: updated system prompts, input validation rules
  • Continuous learning: honeypot gets smarter about emerging attack vectors
  • Integration with existing security tools (SIEM, WAF, API gateways)

Core Mechanism

  • Realistic decoy LLM endpoints with tempting but fake data/capabilities
  • Traffic mirroring: sends copy of production traffic to honeypot for testing
  • Attack taxonomy classifier: categorizes attempts (injection, exfiltration, jailbreak, etc.)
  • Behavioral analysis: learns attacker techniques and tool signatures
  • Automatic patch generation: AI creates defensive prompts based on observed attacks
  • Threat intelligence sharing: anonymized attack patterns shared across customer base
  • Red team mode: Runs automated attacks against your own endpoints
  • Dashboard showing attack frequency, severity, and mitigation recommendations

Monetization Strategy

  • Starter tier: $499/month (1 honeypot endpoint, basic attack detection)
  • Growth tier: $2,500/month (5 honeypots, advanced analytics, API access)
  • Enterprise tier: $10k+/month (unlimited honeypots, custom deployment, dedicated security researcher)
  • Managed red teaming service: $15k-50k per engagement (expert-led penetration testing)
  • Threat intelligence feed: $1,500/month add-on for real-time attack pattern database
  • Incident response retainer: $5k/month for priority support during active attacks

Viral Growth Angle

  • Public attack database: "Latest prompt injection techniques observed in the wild"
  • Free security scanner: "Test your LLM endpoint in 60 seconds"
  • Monthly threat reports: "Top 10 AI attacks this month" (shared widely on security Twitter)
  • Bug bounty program: Pay researchers who find novel attacks via honeypots
  • Conference talks: "We analyzed 1M prompt injection attempts—here's what we learned"
  • Open-source defensive prompt library: Community-contributed mitigations
  • Security certifications: "AI Red Team Tested" badge for customer apps

Existing projects

  • Lakera - Prompt injection detection (reactive, not honeypot-based)
  • Robust Intelligence - AI security platform, different approach
  • Garak - Open-source LLM vulnerability scanner (manual)
  • HiddenLayer - ML security platform focused on model protection
  • Traditional honeypot vendors (e.g., Thinkst Canary) - not AI-specific
  • Manual red teaming consultants charging $50k-200k per engagement

Evaluation Criteria

  • Emotional Trigger: Limit risk (prevent security breaches), be prescient (know attacks before they happen), be first (new security category)
  • Idea Quality: Rank: 8/10 - High emotional intensity (fear of AI security incidents + FOMO on emerging threat), growing urgency as LLM adoption increases
  • Need Category: Trust & Differentiation Needs (security, reliability) + Growth & Innovation Needs (staying ahead of threats)
  • Market Size: $3B+ (subset of $150B+ cybersecurity market, targeting companies with LLM APIs—rapidly expanding)
  • Build Complexity: High - Requires deep AI security expertise, realistic honeypot design, attack analysis ML, but builds on known security patterns
  • Time to MVP: 12-16 weeks with AI coding agents (basic honeypot + attack detection), 24-30 weeks without
  • Key Differentiator: Only proactive AI security platform using honeypot methodology—flips from "waiting to be attacked" to "learning from decoys" before production impact