What is BloodHound-MCP?
BloodHound-MCP is an integration that enhances BloodHound, a tool for Active Directory security analysis, by incorporating Claude AI through the Model Control Panel (MCP). This allows users to analyze BloodHound data using natural language, making complex attack path analysis more accessible.
How to use BloodHound-MCP?
To use BloodHound-MCP, clone the repository from GitHub, install the necessary dependencies, and configure the MCP server with your BloodHound and Neo4j database settings. You can then query BloodHound data using natural language through the MCP interface.
Key features of BloodHound-MCP?
- Natural language interface for querying BloodHound data
- Comprehensive analysis categories including privilege escalation paths and Kerberos security issues
- Ability to generate detailed security reports
- Integration with Neo4j for data storage and analysis
Use cases of BloodHound-MCP?
- Identifying attack paths from vulnerable users to Domain Admins.
- Assessing Active Directory security posture efficiently.
- Generating reports for stakeholders on security vulnerabilities.
FAQ from BloodHound-MCP?
- Is BloodHound-MCP the first AI integration for BloodHound?
Yes, it is the first integration that connects BloodHound with AI through MCP.
- What are the prerequisites for using BloodHound-MCP?
You need BloodHound 4.x+, a Neo4j database with BloodHound data, Python 3.8 or higher, and Claude API access.
- Can I use BloodHound-MCP for legitimate security assessments?
Yes, it is designed for legitimate security assessment purposes, but always ensure you have proper authorization.
What is BloodHound-MCP?
BloodHound-MCP is an integration that enhances BloodHound, a tool for Active Directory security analysis, by incorporating Claude AI through the Model Control Panel (MCP). This allows users to analyze BloodHound data using natural language, making complex attack path analysis more accessible.
How to use BloodHound-MCP?
To use BloodHound-MCP, clone the repository from GitHub, install the necessary dependencies, and configure the MCP server with your BloodHound and Neo4j database settings. You can then query BloodHound data using natural language through the MCP interface.
Key features of BloodHound-MCP?
- Natural language interface for querying BloodHound data
- Comprehensive analysis categories including privilege escalation paths and Kerberos security issues
- Ability to generate detailed security reports
- Integration with Neo4j for data storage and analysis
Use cases of BloodHound-MCP?
- Identifying attack paths from vulnerable users to Domain Admins.
- Assessing Active Directory security posture efficiently.
- Generating reports for stakeholders on security vulnerabilities.
FAQ from BloodHound-MCP?
- Is BloodHound-MCP the first AI integration for BloodHound?
Yes, it is the first integration that connects BloodHound with AI through MCP.
- What are the prerequisites for using BloodHound-MCP?
You need BloodHound 4.x+, a Neo4j database with BloodHound data, Python 3.8 or higher, and Claude API access.
- Can I use BloodHound-MCP for legitimate security assessments?
Yes, it is designed for legitimate security assessment purposes, but always ensure you have proper authorization.