fraud-detection-mcp
Verified Safeby marc-shade
Overview
Provides advanced fraud detection and anomaly analysis for financial transactions, leveraging machine learning, behavioral biometrics, and graph neural networks for financial security.
Installation
uvicorn server_v2:mcp --host 0.0.0.0 --port 8000Environment Variables
- ENVIRONMENT
- DEBUG
- JWT_SECRET_KEY
- JWT_ALGORITHM
- ACCESS_TOKEN_EXPIRE_MINUTES
- API_KEY_HEADER
- REDIS_URL
- DATABASE_URL
- LOG_LEVEL
- ENABLE_METRICS
- METRICS_PORT
- RATE_LIMIT_FREE_TIER
- RATE_LIMIT_PAID_TIER
- RATE_LIMIT_ENTERPRISE
- ISOLATION_FOREST_CONTAMINATION
- XGBOOST_N_ESTIMATORS
- THRESHOLD_HIGH_AMOUNT
- THRESHOLD_CRITICAL_RISK
- THRESHOLD_HIGH_RISK
- MLFLOW_TRACKING_URI
- MLFLOW_EXPERIMENT_NAME
Security Notes
A comprehensive, production-grade security layer is implemented and well-documented (security.py, SECURITY_AUDIT.md, SECURITY_IMPLEMENTATION_SUMMARY.md). It adheres to OWASP best practices, including JWT and API key authentication, Role-Based Access Control (RBAC), Redis-backed rate limiting, OWASP-compliant password validation (bcrypt with 12 rounds), and robust input sanitization against common injection attacks (SQL, XSS, null bytes). Security headers (HSTS, CSP, X-Frame-Options) are configured. Account lockout, token revocation, and dependency pinning are also in place. The system shows no signs of 'eval', obfuscation, or malicious patterns. Minor recommendations for future enhancements (e.g., MFA) are noted in the audit, but do not indicate critical vulnerabilities.
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