We actively support the following versions of the Embedding Vector Database (RAG Module) with security updates:

We take security vulnerabilities seriously. If you discover a security vulnerability in this project, please report it responsibly.

1. DO NOT create a public GitHub issue for security vulnerabilities
2. Email security reports to: [security@your-domain.com]
3. Include the following information:
   • Description of the vulnerability
   • Steps to reproduce the issue
   • Potential impact assessment
   • Suggested fix (if available)

• Acknowledgment within 48 hours of your report
• Initial assessment within 5 business days
• Regular updates on our progress
• Credit in our security advisory (if desired)

• Critical vulnerabilities: Patch within 7 days
• High severity: Patch within 14 days
• Medium/Low severity: Patch within 30 days

• SQLite databases are stored locally on your filesystem
• Embedding models are loaded into local memory
• No data is transmitted to external services by default

• Connection strings should use environment variables
• Database credentials should never be hardcoded
• SSL/TLS encryption is recommended for production deployments

The module implements comprehensive input validation:

// Text input sanitization
request.text = InputValidator.sanitizeText(request.text);

// Table name validation (prevents SQL injection)
request.tablename = InputValidator.validateTableName(request.tablename);

// ID validation (ensures non-negative integers)
request.id = InputValidator.validateId(request.id);

• Parameterized queries are used throughout the codebase
• Table names are validated against a strict regex pattern
• User input is sanitized before database operations

• Verify checksums of downloaded embedding models
• Use trusted sources like Hugging Face for model downloads
• Scan models for malicious content if from untrusted sources

• Models are disposed properly when closing the module
• Memory leaks are prevented through proper resource cleanup
• Singleton pattern prevents multiple model instances

• File permissions should be restricted to the application user
• Database encryption can be implemented using SQLCipher
• Backup security should be considered for sensitive data

• Use connection pooling with proper limits
• Implement proper authentication and authorization
• Enable SSL/TLS for network connections
• Regular security updates for MySQL server

# Use environment variables for sensitive configuration
export DB_HOST=localhost
export DB_USER=your_user
export DB_PASSWORD=your_secure_password
export MODEL_PATH=/secure/path/to/model.gguf

• Use secrets management systems (AWS Secrets Manager, HashiCorp Vault)
• Implement proper logging without exposing sensitive data
• Regular security audits of dependencies
• Container security if using Docker

# Check for security vulnerabilities
npm audit

# Fix automatically fixable vulnerabilities
npm audit fix

# Update dependencies regularly
npm update

• Monitor security advisories for dependencies
• Subscribe to security notifications for Node.js and npm
• Use tools like Snyk or GitHub Dependabot

// ✅ Good: Use environment variables
const rag = new RAGModule({
  modelPath: process.env.MODEL_PATH,
  databasePath: process.env.DB_PATH,
  logLevel: process.env.LOG_LEVEL as LogLevel
});

// ❌ Bad: Hardcoded paths
const rag = new RAGModule({
  modelPath: '/home/user/secret-model.gguf',
  databasePath: '/var/db/sensitive-data.db'
});

// ✅ Good: Generic error messages for users
catch (error) {
  logger.error('Database operation failed', { error: error.message });
  throw new RAGError('Operation failed', ErrorCodes.DATABASE_ERROR);
}

// ❌ Bad: Exposing internal details
catch (error) {
  throw new Error(`Database failed: ${error.stack}`);
}

// ✅ Good: Log without sensitive data
logger.info('User search completed', { 
  userId: user.id, 
  resultCount: results.length 
});

// ❌ Bad: Logging sensitive information
logger.info('Search completed', { 
  query: sensitiveQuery, 
  userEmail: user.email 
});

• All dependencies are up to date
• No hardcoded credentials in code
• Environment variables are properly configured
• Database connections use SSL/TLS
• File permissions are properly restricted
• Logging doesn't expose sensitive data
• Error messages don't reveal internal details
• Input validation is comprehensive
• Model files are from trusted sources

• Monitor security advisories
• Update dependencies regularly
• Review access logs
• Audit database permissions
• Check for unused dependencies
• Validate backup security
• Test disaster recovery procedures

We follow responsible disclosure practices:

1. Report received - We acknowledge receipt
2. Investigation - We investigate and validate the issue
3. Fix development - We develop and test a fix
4. Release preparation - We prepare a security release
5. Public disclosure - We publish the security advisory
6. Credit given - We credit the reporter (if desired)

Security advisories will be published:

• On our GitHub repository
• In our release notes
• Through npm security advisories
• On our project website

For security-related questions or concerns:

• Email: security@your-domain.com
• PGP Key: [Link to PGP key if available]
• Response Time: Within 48 hours

Thank you for helping keep the Embedding Vector Database project secure! 🔒