Security Best Practices for LLM-MCP

This guide provides comprehensive security recommendations for deploying and operating the LLM Platform Model Context Protocol (LLM-MCP) in production environments. Following these best practices will help protect your AI model orchestration infrastructure, sensitive data, and API endpoints.

Table of Contents

• Authentication and Authorization
• API Security
• Data Protection
• Network Security
• Secure Deployment
• Monitoring and Auditing
• Model and Tool Security
• Compliance Considerations
• Security Checklist

Authentication and Authorization

Strong Authentication

LLM-MCP supports multiple authentication methods. We recommend implementing:

{
  "security": {
    "authentication": {
      "primary": "jwt",
      "jwt": {
        "secret": "use_environment_variable",
        "expiresIn": "1h",
        "algorithm": "HS256",
        "issuer": "llm-mcp"
      },
      "apiKey": {
        "enabled": true,
        "header": "X-API-Key",
        "keyRotationDays": 90
      },
      "oauth": {
        "enabled": false,
        "providers": []
      }
    }
  }
}

Best Practices:

1. Never hardcode secrets in configuration files. Use environment variables or secret management solutions.
2. Rotate JWT secrets regularly (at least every 90 days).
3. Implement short token expiration times (1 hour or less) and use refresh tokens.
4. Consider OAuth2 for enterprise deployments with existing identity providers.
5. Use strong API key generation (min 32 bytes of entropy).

Role-Based Access Control (RBAC)

LLM-MCP provides a robust RBAC system to control access to resources:

{
  "security": {
    "rbac": {
      "enabled": true,
      "defaultPolicy": "deny",
      "roles": {
        "admin": {
          "permissions": ["*"]
        },
        "modelUser": {
          "permissions": [
            "model:read",
            "model:execute"
          ]
        },
        "toolUser": {
          "permissions": [
            "tool:read",
            "tool:execute"
          ]
        },
        "readonly": {
          "permissions": [
            "*:read"
          ]
        }
      }
    }
  }
}

Best Practices:

1. Follow principle of least privilege - grant minimal permissions needed.
2. Use deny by default policy.
3. Create granular roles for different user types.
4. Separate model execution from administration permissions.
5. Regularly audit role assignments.

API Security

Rate Limiting

Protect your LLM-MCP instances from abuse and denial of service:

{
  "server": {
    "rateLimiting": {
      "enabled": true,
      "windowMs": 60000,
      "maxRequestsPerWindow": 100,
      "skipAuthenticatedRequests": false,
      "skipPrivateNetworks": true,
      "headers": true,
      "keyGenerator": "ip"
    }
  }
}

Best Practices:

1. Enable rate limiting on all production deployments.
2. Set appropriate limits based on expected legitimate traffic.
3. Consider rate limiting authenticated users to prevent token abuse.
4. Implement graduated rate limiting for different endpoints and users.
5. Monitor rate limit violations as potential security incidents.

Input Validation

LLM-MCP validates inputs, but you should enforce additional controls:

{
  "security": {
    "inputValidation": {
      "strictMode": true,
      "maxRequestSize": "1mb",
      "sanitize": true,
      "parameterValidation": true
    }
  }
}

Best Practices:

1. Validate all inputs on both client and server sides.
2. Limit request body size to prevent DOS attacks.
3. Sanitize all user inputs to prevent injection attacks.
4. Use schema validation for all API payloads.
5. Log validation failures as potential security incidents.

Data Protection

Encryption

Protect data at rest and in transit:

{
  "security": {
    "encryption": {
      "dataAtRest": {
        "enabled": true,
        "algorithm": "aes-256-gcm",
        "keyRotationDays": 30
      },
      "sensitiveFields": [
        "apiKeys",
        "credentials",
        "systemPrompts"
      ]
    },
    "tls": {
      "enabled": true,
      "cert": "/path/to/cert.pem",
      "key": "/path/to/key.pem",
      "minVersion": "TLSv1.2",
      "ciphers": "TLS_AES_128_GCM_SHA256:TLS_AES_256_GCM_SHA384"
    }
  }
}

Best Practices:

1. Always enable TLS for all production deployments.
2. Use TLS 1.2 or higher with strong cipher suites.
3. Encrypt sensitive data at rest in databases and vector stores.
4. Implement regular key rotation for encryption keys.
5. Keep certificates up to date and use auto-renewal if possible.

Data Minimization and Retention

Implement policies to limit data exposure:

{
  "security": {
    "dataRetention": {
      "enabled": true,
      "logRetentionDays": 30,
      "requestHistoryDays": 7,
      "modelOutputsRetentionDays": 3
    }
  }
}

Best Practices:

1. Only store necessary data for the minimum time required.
2. Implement automatic data purging based on retention policies.
3. Anonymize logs and metrics where possible.
4. Consider privacy regulations (GDPR, CCPA, etc.) for data storage.
5. Provide data export and deletion mechanisms.

Network Security

Firewalls and Network Policies

Restrict network access to LLM-MCP services:

{
  "server": {
    "networking": {
      "trustedProxies": ["10.0.0.0/8"],
      "allowedOrigins": ["https://yourdomain.com"],
      "bindAddress": "127.0.0.1"
    }
  }
}

Best Practices:

1. Use network segmentation to isolate LLM-MCP services.
2. Implement IP allowlisting for administrative access.
3. Configure firewalls to restrict traffic to necessary ports only.
4. Use reverse proxies (like Nginx) for public-facing services.
5. Implement proper CORS policies to prevent cross-origin attacks.

Service Communication

Secure communication between LLM-MCP components:

{
  "microservices": {
    "communication": {
      "encryption": true,
      "mutualTLS": true,
      "grpc": {
        "tls": true,
        "clientCert": "/path/to/client/cert.pem",
        "clientKey": "/path/to/client/key.pem",
        "caCert": "/path/to/ca/cert.pem"
      }
    }
  }
}

Best Practices:

1. Use mutual TLS for service-to-service communication.
2. Implement service mesh for large deployments.
3. Use secure service discovery mechanisms.
4. Isolate internal services from public networks.
5. Monitor service communication for anomalies.

Secure Deployment

Container Security

When deploying LLM-MCP in containers:

# Example Dockerfile with security best practices
FROM node:18-alpine AS builder

# Create non-root user
RUN addgroup -S llm-mcp && adduser -S llm-mcp -G llm-mcp

# Install dependencies
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

# Copy application code
COPY . .

# Set proper permissions
RUN chown -R llm-mcp:llm-mcp /app

# Use non-root user
USER llm-mcp

# Set secure runtime options
CMD ["node", "--no-experimental-fetch", "--disallow-code-generation-from-strings", "src/index.js"]

Best Practices:

1. Use minimal base images like Alpine Linux.
2. Run containers as non-root users.
3. Implement read-only file systems where possible.
4. Scan container images for vulnerabilities.
5. Use container runtime security tools.
6. Limit container capabilities using security contexts.

Kubernetes Security

For Kubernetes deployments:

# Example Kubernetes security context
apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-mcp
spec:
  template:
    spec:
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 1000
        readOnlyRootFilesystem: true
      containers:
        - name: llm-mcp
          securityContext:
            allowPrivilegeEscalation: false
            capabilities:
              drop: ["ALL"]
          resources:
            limits:
              cpu: "1"
              memory: "1Gi"

Best Practices:

1. Use NetworkPolicies to restrict pod communication.
2. Implement pod security policies or admission controllers.
3. Use secure service accounts with minimal permissions.
4. Encrypt Kubernetes secrets using key management services.
5. Regularly update Kubernetes components.
6. Implement resource quotas to prevent DOS attacks.

Monitoring and Auditing

Security Logging

Configure comprehensive security logging:

{
  "logging": {
    "security": {
      "enabled": true,
      "level": "info",
      "sensitiveFields": ["password", "token", "key"],
      "authEvents": true,
      "accessEvents": true,
      "rateLimitEvents": true,
      "validationFailures": true
    }
  }
}

Best Practices:

1. Log all security-relevant events including authentication, authorization, and access.
2. Mask sensitive data in logs.
3. Use structured logging formats (JSON) for better analysis.
4. Implement centralized log management using tools like ELK stack.
5. Set up log retention and archival policies.

Security Monitoring

Implement real-time security monitoring:

{
  "monitoring": {
    "security": {
      "bruteForceDetection": {
        "enabled": true,
        "maxAttempts": 5,
        "windowSeconds": 300,
        "blockDurationMinutes": 15
      },
      "anomalyDetection": {
        "enabled": true,
        "sensitivityLevel": "medium"
      }
    }
  }
}

Best Practices:

1. Monitor for authentication failures and potential brute force attacks.
2. Implement automatic blocking of suspicious IP addresses.
3. Set up alerts for unusual activity patterns.
4. Conduct regular security scanning of your environment.
5. Integrate with SOC tools for enterprise environments.

Model and Tool Security

Tool Validation

Secure registered tools in LLM-MCP:

{
  "tools": {
    "security": {
      "validationEnabled": true,
      "sandboxing": true,
      "resourceLimits": {
        "cpu": 0.1,
        "memory": "128M",
        "executionTimeMs": 5000
      },
      "allowedAPIs": [
        "http",
        "https",
        "llm-mcp:internal"
      ],
      "disallowedNodeAPIs": [
        "fs",
        "child_process",
        "crypto"
      ]
    }
  }
}

Best Practices:

1. Validate all tool implementations before registration.
2. Run tools in sandboxed environments to prevent escapes.
3. Implement resource limits for tool execution.
4. Restrict available APIs to necessary functions only.
5. Monitor tool execution for anomalies.

Model Security

Protect access to models and their outputs:

{
  "models": {
    "security": {
      "outputFiltering": true,
      "sensitivePromptDetection": true,
      "contentSafety": {
        "enabled": true,
        "level": "medium"
      },
      "promptInjectionDetection": true
    }
  }
}

Best Practices:

1. Implement prompt validation to prevent injection attacks.
2. Use content filtering for model outputs when appropriate.
3. Rate limit model access to prevent abuse.
4. Monitor for sensitive data in prompts and outputs.
5. Implement model access controls based on sensitivity.

Compliance Considerations

Audit Trails

Maintain comprehensive audit trails for compliance:

{
  "compliance": {
    "auditTrail": {
      "enabled": true,
      "detailLevel": "high",
      "retention": {
        "days": 365,
        "storageLocation": "s3://your-bucket/audit-logs"
      },
      "events": [
        "authentication",
        "authorization",
        "modelExecution",
        "toolExecution",
        "configChange"
      ]
    }
  }
}

Best Practices:

1. Log all compliance-relevant events with sufficient detail.
2. Maintain audit logs for the required retention period.
3. Ensure log integrity with tamper-proof storage.
4. Implement log backup procedures.
5. Provide easy audit log export for compliance reviews.

Regulatory Compliance

Address regulatory requirements:

{
  "compliance": {
    "regulations": {
      "gdpr": {
        "enabled": true,
        "dataSubjectAccessEnabled": true,
        "dataDeletionEnabled": true
      },
      "hipaa": {
        "enabled": false
      }
    }
  }
}

Best Practices:

1. Identify applicable regulations for your deployment.
2. Implement controls required by each regulation.
3. Document compliance measures and evidence.
4. Conduct regular compliance reviews.
5. Consider third-party compliance audits for critical systems.

Security Checklist

Use this checklist for new LLM-MCP deployments:

• Authentication: Proper authentication method configured
• Secrets Management: No hardcoded secrets in configuration
• RBAC: Role-based access controls implemented
• TLS: HTTPS enabled for all endpoints
• Rate Limiting: Configured to prevent abuse
• Input Validation: Enabled for all API endpoints
• Data Encryption: Sensitive data encrypted at rest
• Network Security: Firewalls and network policies in place
• Service Security: Secure service-to-service communication
• Container Security: Non-root user, minimal image
• Logging: Security events properly logged
• Monitoring: Security monitoring and alerts configured
• Tool Security: Tool validation and sandboxing enabled
• Model Security: Access controls and monitoring in place
• Compliance: Audit trails and regulatory controls configured
• Backup: Secure backup procedures implemented
• Incident Response: Security incident plan documented
• Vulnerability Management: Regular scanning and patching

Additional Resources

• OWASP API Security Top 10
• Kubernetes Security Best Practices
• Node.js Security Best Practices
• NIST Cybersecurity Framework
• Docker Security Best Practices

See Also

• Architecture Overview - System architecture and design
• Advanced Configuration - Comprehensive configuration options
• Monitoring and Logging - Monitoring and observability guide
• API Reference - Complete API documentation
• Deployment Guide - Deployment options and instructions

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This guide provides a foundation for securing your LLM-MCP deployment. Security is an ongoing process that requires regular assessment and updates to address emerging threats and vulnerabilities.