Advanced Prompt Injection Defense

[mheadd]

Advanced Prompt Injection Defense

Issue Summary

Implement advanced security measures to further harden the Government AI Prototype against sophisticated prompt injection attacks. This builds upon the foundational security implemented in Phase 1 (Input Validation, Context Isolation, Response Filtering).

Current State (Phase 1 - Finished)

• Input Validation & Sanitization (blocks 80% of known injection patterns)
• Context Isolation with security boundaries
• Response validation and filtering
• Security monitoring and metrics tracking
• Comprehensive test suite with 15+ injection test cases

Current Defense Effectiveness: ~80% against common prompt injection attacks

Proposed Enhancements

Phase 2: Intermediate Defenses (Recommended for next implementation)

1. Rate Limiting & User Tracking

Priority: 🔴 High | Effort: Medium | Impact: +15% effectiveness

Implementation Requirements:

• IP-based rate limiting (configurable requests/minute)
• Suspicious activity tracking and progressive blocking
• User session management and behavior analysis
• Whitelist capability for trusted sources

Technical Details:

// New files to create:
- api/middleware/rate-limiter.js
- api/middleware/user-tracker.js
- api/config/security-config.js

// Environment variables:
RATE_LIMIT_WINDOW=900000          # 15 minutes
RATE_LIMIT_MAX_REQUESTS=50        # Requests per window
RATE_LIMIT_BLOCK_DURATION=3600000 # 1 hour block
SECURITY_TRACKING_ENABLED=true

Benefits:

• Prevent automated attack campaigns
• Identify and block repeat offenders
• Protect against DoS via security validation overhead
• Behavioral analysis for threat detection

2. Enhanced Response Filtering

Priority: 🟡 Medium | Effort: Low | Impact: +10% effectiveness

Implementation Requirements:

• Semantic analysis of response content
• Persona consistency validation
• Content appropriateness scoring
• Advanced pattern detection for subtle character breaks

Technical Details:

// Enhancements to existing functions:
- validateResponse() - Add semantic analysis
- New: semanticResponseAnalysis()
- New: personaConsistencyCheck()

3. Dual-Prompt Architecture

Priority: 🟡 Medium | Effort: High | Impact: +20% effectiveness

Implementation Requirements:

• Pre-screening LLM for intent classification
• Fast injection attempt detection
• Request routing based on risk assessment
• Fallback strategies for different threat levels

Technical Details:

// New architecture components:
- api/security/intent-classifier.js
- api/security/risk-assessor.js
- Lightweight model integration (e.g., DistilBERT)

Phase 3: Advanced Defenses (Future consideration)

4. Semantic Analysis Engine

Priority: 🟢 Low | Effort: Very High | Impact: +25% effectiveness

Implementation Requirements:

• Embedding-based similarity detection
• Machine learning injection classifier
• Vector database for known attack patterns
• Real-time model inference capabilities

5. Multi-Layer Defense System

Priority: 🟡 Medium | Effort: Very High | Impact: +30% effectiveness

Implementation Requirements:

• Multiple validation stages with different techniques
• Consensus-based filtering decisions
• Integration with external threat intelligence
• Adaptive threshold adjustment

Technical Implementation Plan

Phase 2A: Rate Limiting (Sprint 1)

# Files to create/modify:
api/middleware/rate-limiter.js
api/middleware/user-tracker.js
api/server.js (integrate middleware)
api/__tests__/rate-limiting.test.js

Phase 2B: Enhanced Filtering (Sprint 2)

# Files to modify:
api/server.js (enhance validateResponse)
api/security/semantic-analyzer.js (new)
api/__tests__/response-filtering.test.js

Phase 2C: Dual-Prompt Architecture (Sprint 3-4)

# Files to create:
api/security/intent-classifier.js
api/security/risk-assessor.js
api/config/classification-config.yaml
api/__tests__/dual-prompt.test.js

Success Metrics

Phase 2 Targets:

• Block Rate: 90% of injection patterns (up from 80%)
• False Positive Rate: <0.5% for legitimate questions
• Response Time Impact: <10ms additional overhead
• Advanced Threat Detection: 95% of sophisticated attempts

Monitoring Requirements:

• Security dashboard with real-time metrics
• Attack pattern trend analysis
• Performance impact tracking
• False positive/negative analysis

Testing Requirements

New Test Categories:

// Enhanced test coverage needed:
describe('Rate Limiting Defense', () => {
  // IP-based rate limiting tests
  // Progressive blocking validation
  // Whitelist functionality tests
});

describe('Semantic Analysis', () => {
  // Intent classification accuracy
  // Novel injection pattern detection
  // Performance benchmarking
});

describe('Advanced Integration', () => {
  // Multi-layer defense coordination
  // End-to-end attack simulation
  // Stress testing under load
});

Security Considerations

New Attack Vectors to Address:

• Distributed injection attempts (multiple IPs)
• Time-delayed injection sequences
• Semantic manipulation (meaning preservation with different words)
• Context-aware injection (persona-specific attacks)
• Adversarial prompt engineering

Privacy & Compliance:

• IP address handling and retention policies
• User tracking data minimization
• GDPR compliance for EU users
• Audit logging for security events

Expected Benefits

Security Improvements:

• 90%+ effectiveness against known injection patterns
• Proactive defense against novel attack techniques
• Real-time adaptation to emerging threats
• Enterprise-grade security posture

Operational Benefits:

• Detailed attack intelligence and trends
• Automated threat response capabilities
• Reduced manual security monitoring overhead
• Improved incident response capabilities

Dependencies & Prerequisites

External Dependencies:

• Rate limiting middleware (express-rate-limit or custom)
• Caching layer (Redis recommended for distributed rate limiting)
• Optional: Lightweight ML model for intent classification
• Optional: Vector database for semantic analysis (Pinecone, Weaviate)

Infrastructure Requirements:

• Persistent storage for user tracking data
• Caching layer for rate limiting state
• Monitoring and alerting integration
• Optional: Separate security service deployment

Implementation Notes

Backward Compatibility:

• All existing security features must remain functional
• New security layers should be configurable/toggleable
• Graceful degradation if advanced features fail
• Maintain existing API response formats

Performance Considerations:

• Security overhead should not exceed 10ms per request
• Rate limiting should use efficient data structures
• Semantic analysis should be optimized for speed
• Consider caching for frequently analyzed patterns