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BloodHound MCP

by stevenyu113228
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Python
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BloodHound MCP

BloodHound MCP（模型上下文协议）是 BloodHound 工具的创新扩展，旨在使大型语言模型 (LLM) 能够通过自然语言查询与 Active Directory (AD) 和 Azure Active Directory (AAD) 环境进行交互并进行分析。BloodHound MCP 利用 LLM 的强大功能，允许用户使用简单的对话命令执行复杂的查询并从其 AD/AAD 环境中检索洞察。

特征

  • 自然语言查询：使用对话语言查询您的 AD/AAD 环境，而无需手动编写 Cypher 查询。
  • LLM 支持的分析：利用大型语言模型的功能来代表您解释和执行查询。
  • 无缝集成：与 Neo4j 中存储的现有 BloodHound 数据协同工作，为复杂分析提供用户友好的界面。
  • 可定制：轻松配置系统以适应您的特定环境和工具。

配置 MCP 服务器

{ "mcpServers": { "BloodHound": { "name": "BloodHound", "isActive": true, "command": "uv", "args": [ "run", "--with", "mcp[cli],neo4j", "mcp", "run", "<PATH_TO_THE_PROJECT>server.py" ], "env": { "BLOODHOUND_URI": "bolt://localhost:7687", "BLOODHOUND_USERNAME": "neo4j", "BLOODHOUND_PASSWORD": "bloodhound" } } } }

用法

配置

要自定义 BloodHound MCP，请在 MCP 支持的工具中更新配置文件。主要设置包括：

  • Neo4j 数据库连接：
    • BLOODHOUND_URI ：Neo4j 数据库的 URI（例如，bolt://localhost:7687）。
    • BLOODHOUND_USERNAME ：您的 Neo4j 用户名。
    • BLOODHOUND_PASSWORD ：您的 Neo4j 密码。
  • 服务器设置：调整命令和参数以匹配您的环境和工具要求。

贡献

欢迎为 BloodHound MCP 做出贡献！参与方式：

  1. 分叉存储库：在 GitHub 上创建您自己的副本。
  2. 创建分支：在新分支中处理您的功能或修复。
  3. 提交拉取请求：包括对更改的清晰描述。

特别感谢

自定义查询来自： https://github.com/CompassSecurity/BloodHoundQueries

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security – no known vulnerabilities
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quality - confirmed to work

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

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一种扩展，允许大型语言模型通过自然语言查询而不是手动 Cypher 查询与 Active Directory 环境进行交互和分析。

  1. 特征
    1. 配置 MCP 服务器
      1. 用法
        1. 配置
          1. 贡献
            1. 特别感谢

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