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AI Security Honeypot: Detect Prompt Injection and Jailbreak Attempts in Real-Time

Security vulnerabilities in satellites and DDoS botnets dominate headlines. AI systems face similar attacks through prompt injection and jailbreaks, but companies have no visibility into these threats.

App Concept

  • Security monitoring platform that detects adversarial attacks on LLM applications
  • Honeypot endpoints mimic production APIs to attract and analyze attack patterns
  • Real-time threat detection using ML-trained classifiers for prompt injection patterns
  • Automated response playbooks (rate limiting, IP blocking, context sanitization)
  • Threat intelligence sharing across customer base (anonymized attack signatures)
  • Integration with SIEM tools (Splunk, Datadog, Elastic) and WAF systems
  • Compliance reporting for SOC2, ISO 27001 showing AI-specific security controls

Core Mechanism

  • SDK wraps LLM endpoints, analyzing all inputs for attack signatures before processing
  • Pattern library of 10K+ known jailbreak attempts, prompt injections, data extraction tricks
  • Behavioral analysis flags unusual patterns (excessive retries, role-playing scenarios, encoding tricks)
  • Honeypot APIs run intentionally vulnerable models to collect new attack vectors
  • Machine learning continuously updates attack detection from honeypot observations
  • Threat severity scoring (0-100) based on potential impact and sophistication
  • Automated incident response: log, alert, block, or sanitize based on policies
  • Security dashboard shows attack timeline, top vectors, geographic distribution
  • Red team toolkit helps companies test their own AI security posture

Monetization Strategy

  • Free tier: Basic attack detection, 10K requests/month, 30-day logs
  • Pro ($299/mo): Advanced detection, 500K requests, 1-year logs, SIEM integration
  • Enterprise ($1,499+/mo): Unlimited requests, custom rules, threat intelligence sharing
  • Managed security service: 24/7 SOC monitoring by AI security experts ($5K+/mo)
  • Penetration testing services: Red team assessments of AI systems ($15K+ per engagement)
  • Insurance partnerships: Discounted cyber insurance for customers using platform

Viral Growth Angle

  • Public "AI Attack Observatory" showing real-time global threats (anonymized)
  • Monthly threat reports become industry standard reference
  • Open-source prompt injection test suite drives GitHub stars and adoption
  • Bug bounty program pays for novel jailbreak techniques discovered
  • Integration with popular frameworks (LangChain, AutoGPT) as security layer
  • Conference talks and OWASP AI Security Project collaboration
  • "AI Security Scorecard" free tool attracts enterprise leads

Existing projects

Evaluation Criteria

  • Emotional Trigger: Limit risk (prevent security breaches), be indispensable (required for production AI)
  • Idea Quality: Rank: 9/10 - Critical emerging need; inspired by satellite security and DDoS stories showing infrastructure vulnerability
  • Need Category: Stability & Security Needs - Secure model deployment, compliance with regulations
  • Market Size: $3B+ (every company exposing LLMs to users; especially enterprises with security requirements)
  • Build Complexity: High (ML attack detection, honeypot infrastructure, SIEM integrations, real-time processing)
  • Time to MVP: 10-12 weeks with AI coding (basic detection + honeypot + dashboard + 1 SIEM integration)
  • Key Differentiator: Only platform combining honeypot-based threat intelligence with production protection specifically for AI systems