Prompt Security Scanner: Injection Detection and Protection for LLM Apps

LLM applications are vulnerable to prompt injection attacks that can leak sensitive data, bypass safety filters, and manipulate AI behavior—but no comprehensive security layer exists.

App Concept

• Real-time middleware that scans all incoming prompts for injection attempts before they reach your LLM
• ML-powered detection of adversarial patterns: jailbreaks, data exfiltration attempts, system prompt leakage
• Automated defense strategies: input sanitization, prompt hardening, output filtering
• Security dashboard tracking attack attempts, patterns, and threat actors
• Compliance reporting for SOC2, GDPR, HIPAA requirements around AI safety
• Post-incident forensics showing exactly how an attack was constructed and what data was accessed

Core Mechanism

• SDK/API proxy sits between your application and LLM providers
• Multi-layer detection: pattern matching (known jailbreaks), semantic analysis (adversarial intent), anomaly detection (unusual input structure)
• Real-time threat intelligence: crowdsourced database of latest injection techniques
• Configurable security policies: block, flag, sanitize, or log suspicious prompts
• Integration with existing security tools (SIEM, incident response platforms)
• Automatic generation of security patches when new attack vectors are discovered
• Red team simulation mode for testing your prompts against known attack patterns
• Output validation to catch leaked system prompts or sensitive data in responses

Monetization Strategy

• Free tier: 1,000 prompts/month scanned, basic injection detection
• Professional: $299/month for 50K prompts, advanced detection, custom rules
• Enterprise: $1,500+/month for unlimited prompts, dedicated threat intelligence, compliance reporting, SSO
• Incident response services: $5K+ for post-breach forensics and remediation
• Security audit services: One-time $10K+ for comprehensive LLM application security review

Viral Growth Angle

• Public "Prompt Injection Hall of Shame" showcasing real attacks (anonymized, with permission)
• Monthly security reports: "We blocked 47K injection attempts in September—here's what we learned"
• Open-source prompt injection test suite that becomes standard for security testing
• Viral demos showing ChatGPT/Claude being manipulated in shocking ways
• Conference presentations revealing industry-wide vulnerability statistics
• Integration with bug bounty platforms rewarding researchers who find novel injection techniques
• "Certified Secure LLM App" badge program for applications that pass security scans

Existing projects

• Rebuff - Open-source prompt injection detection, early stage
• LLM Guard - Security toolkit for LLMs, more focused on content filtering
• NeMo Guardrails - NVIDIA's safety rails framework, complex setup
• Microsoft Prompt Shields - Azure-specific, limited coverage
• Lakera Guard - Prompt injection detection API, newer entrant

Evaluation Criteria

• Emotional Trigger: Limit risk (prevent catastrophic security breaches), be indispensable (become essential security layer)
• Idea Quality: Rank: 8/10 - Critical need as LLM adoption grows, but market still educating itself on prompt injection risks
• Need Category: Stability & Security Needs - Secure model deployment and compliance with privacy regulations
• Market Size: $5B+ (AI security subset of $200B+ cybersecurity market, growing 60% YoY)
• Build Complexity: High - Requires adversarial ML research, real-time threat intelligence, complex pattern detection
• Time to MVP: 4-6 months with AI coding agents (basic pattern matching + SDK), 8-12 months without
• Key Differentiator: Only comprehensive prompt injection protection with real-time detection, automated defenses, and continuous threat intelligence updates