Cyber Sentinel MCP

🚀 Cyber Sentinel MCP Server

Cyber Sentinel MCP Server is a comprehensive threat intelligence aggregation MCP (Model Context Protocol) server. It offers a unified access point to multiple threat intelligence sources, facilitating security analysis.

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• MCP-compatible client (Claude Desktop, Cursor, etc.)

Installation

1. Clone the repository:

git clone https://github.com/jx888-max/cyber-sentinel-mcp.git
cd cyber-sentinel-mcp

2. Install dependencies:

pip install -e .

3. Configure API keys:

# Run the setup wizard
python -m cyber_sentinel.setup_wizard

# Or directly set environment variables
export VIRUSTOTAL_API_KEY=your_virustotal_api_key_here
export ABUSEIPDB_API_KEY=your_abuseipdb_api_key_here

4. Verify installation:

python -c "from cyber_sentinel.server import app; print('✅ Installation successful!')"

API Key Setup

VirusTotal (Highly Recommended)

• Free Tier: 1,000 requests/day
• Capabilities: IP, domain, hash, and URL analysis

1. Visit VirusTotal
2. Create a free account
3. Get your API key from the API section
4. Add to .env: VIRUSTOTAL_API_KEY=your_key_here

AbuseIPDB (Highly Recommended)

• Free Tier: 1,000 requests/day
• Capabilities: IP address reputation and abuse reporting

1. Visit AbuseIPDB
2. Create a free account
3. Get your API key from the account settings
4. Add to .env: ABUSEIPDB_API_KEY=your_key_here

Shodan (Optional)

• Free Tier: 100 results/month
• Capabilities: Internet-connected device intelligence

1. Visit Shodan
2. Create an account and get your API key
3. Add to .env: SHODAN_API_KEY=your_key_here

URLhaus (No API Key Required)

• Free: Works without API key for basic usage
• Capabilities: Malware URL and payload tracking

✨ Features

🔍 Threat Intelligence

• Multi-Source Intelligence: Aggregates data from VirusTotal v3, AbuseIPDB, URLhaus, Shodan, ThreatFox, and MalwareBazaar.
• Smart Indicator Detection: Automatically detects IP addresses, domains, file hashes, and URLs.
• Intelligent Aggregation: Combines results from multiple sources with confidence scoring.
• Async Performance: High-performance concurrent processing.
• Smart Caching: Reduces API calls and improves response times (1-hour TTL).
• Rate Limiting: Respects API limits across all sources (60 req/min default).
• Error Recovery: Graceful handling of API failures and timeouts.

🛡️ Code Security Analysis

• Multi-Language Support: Analyzes Python, JavaScript, Java, C#, PHP, Go, Rust, C++, and SQL code.
• Vulnerability Detection: Identifies hardcoded secrets, SQL injection, XSS, path traversal, and more.
• Network Indicator Analysis: Extracts and analyzes IPs, domains, and URLs found in code.
• Secure Alternatives: Provides secure coding recommendations and alternatives.
• Risk Scoring: Calculates comprehensive security risk scores.

📦 Dependency Security

• Multi-Platform Support: Scans NPM, Python, Maven, Cargo, Go, and Composer dependencies.
• Vulnerability Detection: Identifies known malicious packages and outdated dependencies.
• Security Recommendations: Provides actionable security improvement suggestions.
• Risk Assessment: Comprehensive dependency risk scoring.

🐳 Infrastructure Security

• Docker Security: Analyzes Dockerfile configurations for security best practices.
• Kubernetes Security: Scans K8s manifests for security misconfigurations.
• CI/CD Integration: Provides security analysis for DevOps pipelines.

📊 Reporting & Visualization

• Rich Reports: Generates comprehensive security analysis reports.
• Visual Dashboards: Creates security metrics and trend visualizations.
• Export Options: Supports multiple output formats (JSON, HTML, PDF).
• MCP Protocol: Full compatibility with MCP-enabled AI assistants.

💻 Usage Examples

🔍 Threat Intelligence Analysis

Analyze the IP address 8.8.8.8 for any malicious activity
Check if 1.1.1.1 is safe to use
Is google.com safe?
Check the security status of example.com
Analyze this MD5 hash: d41d8cd98f00b204e9800998ecf8427e
Is this URL safe: https://example.com/suspicious-path
Show me the status of all threat intelligence sources

🛡️ Code Security Analysis

Analyze this Python code for security vulnerabilities:
[paste your code here]

Check this JavaScript function for XSS vulnerabilities:
[paste your code here]

Scan this SQL query for injection risks:
[paste your code here]

📦 Dependency Security Scanning

Scan these project dependencies for vulnerabilities:
package.json: [paste content]
requirements.txt: [paste content]

Check my Python project for outdated packages:
[provide requirements.txt content]

🐳 Infrastructure Security

Analyze this Dockerfile for security issues:
[paste Dockerfile content]

Check this Kubernetes deployment for security misconfigurations:
[paste K8s YAML content]

📊 Security Reporting

Generate a comprehensive security report for my project
Create a security dashboard with current threat landscape
Export security findings to HTML report

📚 Documentation

🛠️ Available MCP Tools

🔍 Threat Intelligence Tools

analyze_indicator

Analyzes security indicators across multiple threat intelligence sources.

Supported Indicators:

• IP Addresses: IPv4 addresses (e.g., 8.8.8.8)
• Domain Names: Any domain (e.g., google.com)
• File Hashes: MD5, SHA1, SHA256 hashes
• URLs: Complete URLs (e.g., https://example.com)

Returns:

• Overall reputation (clean/malicious/unknown)
• Confidence score (0-100%)
• Results from individual threat intelligence sources
• Geographic and ISP information (for IPs)
• Detailed analysis data

check_api_status

Checks the configuration and status of all threat intelligence sources.

Returns:

• API key validation status
• Available capabilities per source
• Rate limiting configuration
• Overall system health

🛡️ Security Analysis Tools

analyze_code_security

Performs comprehensive security analysis of source code.

Parameters:

• code_content: Source code to analyze
• language: Programming language (auto-detected if not specified)
• locale: Output language (zh/en)

Returns:

• Security vulnerabilities and their severity
• Hardcoded secrets and credentials
• Network indicators found in code
• Secure coding recommendations
• Risk score and remediation guidance

scan_project_dependencies

Scans project dependencies for security vulnerabilities.

Parameters:

• project_files: Dictionary of dependency files (package.json, requirements.txt, etc.)

Returns:

• Known malicious packages
• Outdated dependencies with vulnerabilities
• Security recommendations
• Risk assessment and scoring

analyze_docker_security

Analyzes Docker configurations for security best practices.

Parameters:

• dockerfile_content: Dockerfile content to analyze

Returns:

• Security misconfigurations
• Best practice violations
• Hardening recommendations
• Risk assessment

scan_kubernetes_config

Scans Kubernetes manifests for security issues.

Parameters:

• k8s_manifests: Dictionary of Kubernetes YAML files

Returns:

• Security policy violations
• Privilege escalation risks
• Network security issues
• Compliance recommendations

generate_security_report

Generates comprehensive security reports with visualizations.

Parameters:

• analysis_results: Combined results from security analyses
• report_format: Output format (json/html/markdown)

Returns:

• Formatted security report
• Executive summary
• Detailed findings
• Remediation roadmap

📊 Example Response

{
  "indicator": "8.8.8.8",
  "type": "ip",
  "overall_reputation": "clean",
  "confidence": 100.0,
  "sources_checked": 4,
  "sources_responded": 3,
  "malicious_sources": 0,
  "clean_sources": 3,
  "countries": ["US"],
  "isps": ["Google LLC"],
  "detailed_results": [
    {
      "source": "VirusTotal",
      "reputation": "clean",
      "malicious_count": 0,
      "total_engines": 89
    }
  ],
  "errors": [],
  "timestamp": "2024-01-15T10:30:00Z"
}

🔧 Technical Details

⚡ Performance & Reliability

🚀 High Performance

• Async Architecture: High-performance concurrent processing across all analysis tools.
• Smart Caching: 1-hour TTL reduces API calls and improves response times.
• Parallel Processing: Simultaneous analysis across multiple threat intelligence sources.
• Optimized Algorithms: Efficient pattern matching and vulnerability detection.

🛡️ Reliability & Resilience

• Rate Limiting: Configurable limits (default: 60 requests/minute) with intelligent throttling.
• Timeout Handling: 30-second request timeouts prevent hanging operations.
• Error Recovery: Graceful handling of API failures and network issues.
• Fallback Mechanisms: Continues analysis even when some sources are unavailable.
• Retry Logic: Automatic retry with exponential backoff for transient failures.

🔒 Security & Privacy

🛡️ Data Protection

• Zero Data Storage: No indicators, code, or analysis results are permanently stored.
• Memory-Only Processing: All analysis happens in memory with automatic cleanup.
• API Key Security: Keys managed securely through environment variables and encrypted storage.
• Source Isolation: Each threat intelligence source operates independently with isolated credentials.

🔐 Privacy Safeguards

• Local Processing: Code analysis happens locally without external transmission.
• Error Isolation: Sensitive information is never exposed in error messages or logs.
• Audit Trail: Optional security event logging for compliance requirements.
• Data Minimization: Only necessary data is processed and immediately discarded.

🧪 Testing

Run the test suite to verify functionality:

# Run unit tests
python -m pytest tests/ -v

# Test with your API keys
python -c "
from cyber_sentinel.server import check_api_status
import asyncio
print(asyncio.run(check_api_status()))
"

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/amazing-feature).
3. Make your changes and add tests.
4. Commit your changes (git commit -m 'Add amazing feature').
5. Push to the branch (git push origin feature/amazing-feature).
6. Open a Pull Request.

🆘 Support

• Issues: GitHub Issues
• Documentation: See llms-install.md for detailed setup.
• MCP Protocol: Model Context Protocol Documentation

🙏 Acknowledgments

• Anthropic for the MCP protocol and Claude AI.
• VirusTotal for comprehensive malware analysis.
• AbuseIPDB for IP reputation intelligence.
• URLhaus for malware URL tracking.
• Shodan for internet device intelligence.
• ThreatFox for IOC sharing platform.
• MalwareBazaar for malware sample intelligence.
• OWASP for security best practices and vulnerability patterns.
• Open source security community for continuous threat intelligence sharing.

🛡️ Threat Intelligence, Simplified.