This project is currently a demonstration and not production-ready. The following security considerations apply:

1. Simulated Privacy Methods

   • Local LLM processing is simulated (no actual inference)
   • Split learning is not implemented
   • TEE integration is not implemented
   • Differential privacy is not implemented
   • Impact: Privacy guarantees are conceptual only

2. Watermark Privacy

   • Watermarks embed query hash and user fingerprint
   • Risk: If outputs are shared externally, metadata can be extracted
   • Mitigation: Watermarks are for internal tracking only
   • Recommendation: Add UI warning when watermarking is enabled

3. Synthetic Substitution Quality

   • Regex-based entity extraction is fragile
   • Limited synthetic data pools (15 names, 8 orgs, 8 locations)
   • Risk: High false-positive/false-negative rates
   • Mitigation: Input validation limits data size
   • Recommendation: Integrate Microsoft Presidio for production

4. Input Validation

   • Maximum request length: 10,000 characters
   • Maximum data length: 100,000 characters
   • No HTML/script sanitization (client-side only)
   • Risk: Potential XSS if server-side rendering is added
   • Mitigation: Static export prevents server-side execution

5. No Rate Limiting

   • No throttling on API calls
   • Risk: Resource exhaustion, abuse
   • Mitigation: Client-side only (no backend to abuse)
   • Recommendation: Add rate limiting if backend is added

1. Never commit secrets

   • Use .env.local for local development
   • Add .env.local to .gitignore (already done)
   • Use environment variables for API keys

2. Input validation

   • All user inputs are validated for length
   • Add additional validation if extending functionality

3. Dependencies

   • Run npm audit regularly
   • Update dependencies promptly
   • Review security advisories

4. Code review

   • All PRs require review
   • Security-sensitive changes require extra scrutiny

1. Do not share watermarked outputs externally

   • Watermarks contain tracking metadata
   • Can be used to identify users and queries

2. Understand privacy limitations

   • This is a demo, not production software
   • Privacy methods are simulated
   • Do not use for actual sensitive data

3. Keep software updated

   • Update to latest version regularly
   • Review release notes for security fixes

If you discover a security vulnerability, please follow these steps:

1. Do NOT open a public issue
2. Email security details to: [your-security-email@example.com]
3. Include:
   • Description of the vulnerability
   • Steps to reproduce
   • Potential impact
   • Suggested fix (if any)

We will respond within 48 hours and provide a timeline for fixes.

• Integrate actual privacy methods

  • Local LLM (llama.cpp + GGUF)
  • Split learning backend
  • TEE support (AWS Nitro, Azure Confidential)
  • Differential privacy library (Google DP, OpenDP)

• Enhance PII detection

  • Integrate Microsoft Presidio
  • Add custom entity recognizers
  • Improve accuracy to 95%+

• Add security features

  • Rate limiting (10 req/min per user)
  • Input sanitization (HTML/script tags)
  • CSRF protection (if backend added)
  • Content Security Policy headers

• Testing & Auditing

  • Comprehensive test suite (80%+ coverage)
  • Security audit by third party
  • Penetration testing
  • Compliance review (HIPAA, GDPR, etc.)

• Monitoring & Logging

  • Security event logging
  • Anomaly detection
  • Audit trail for sensitive operations
  • Incident response plan

Before deploying to production, ensure:

• All simulated methods replaced with real implementations
• Third-party security audit completed
• Penetration testing performed
• Rate limiting implemented
• Input sanitization comprehensive
• Secrets management in place
• Monitoring and alerting configured
• Incident response plan documented
• Compliance requirements met (HIPAA, GDPR, etc.)
• User documentation includes security warnings
• Privacy policy and terms of service published

• Uses zero-width Unicode characters (U+200B, U+200C)
• Encodes: timestamp (48 bits) + level (3 bits) + hash (16 bits) + fingerprint (8 bits)
• Not cryptographically secure - easily detectable and removable
• Purpose: Internal tracking only, not security feature

For production, consider:

• Cryptographic watermarking (e.g., spread spectrum)
• Digital signatures for output verification
• End-to-end encryption for sensitive data
• Hardware security module (HSM) integration

• OWASP Top 10
• NIST Privacy Framework
• Microsoft Presidio
• Differential Privacy
• Trusted Execution Environments

Last Updated: May 2026

Security Contact: [your-security-email@example.com]