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ramibot

by RamiBotAI·20·综合分 43

RamiBot 是一个本地优先的AI安全平台,集成了MCP支持红蓝队操作。

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概述

RamiBot 通过MCP协议将AI推理与网络安全工具结合,支持多LLM、Docker集成和结构化安全技能管道。平台提供通过rami-kali等MCP服务器的受控工具执行,包含45+种渗透测试工具。其独特功能包括证据锁定报告、工具审批网关和Tor代理管理,构成全面的安全操作工作流。

试试问 AI

装完之后,这里有 3 个你可以让 AI 做的事:

:红队操作中LLM辅助漏洞扫描
:蓝队分析中证据锁定报告生成
:安全研究中Docker容器化工具执行

什么时候选它

当您需要在安全操作中获得 AI 辅助,并在受控环境中执行真实工具时选择 RamiBot,尤其适合需要人工监督敏感操作的红蓝队工作流。

什么时候不要选它

如果您需要基于云的 SaaS 解决方案而不依赖 Docker,或者需要快速部署而无需容器化开销,请不要选择 RamiBot。

此 server 暴露的工具

从 README 抽取出 12 个工具
  • recon

    Security reconnaissance tool for port scanning, subdomain enumeration, and DNS queries

  • exploit

    Exploitation tool for running payloads and achieving remote code execution

  • defense

    Security defense tool for hardening systems and mitigating threats

  • analysis

    Security analysis tool for examining logs, traffic, and forensic data

  • reporting

    Security reporting tool for generating structured security reports

  • burp_expert

    Burp Suite integration for web application security testing

  • docker_terminal

    Access Docker container terminal for running commands

  • tor_proxy

    Manage Tor proxy connection for anonymous browsing

  • cve_query

    Query CVE database for vulnerability information

  • skill_pipeline

    Automatically select and execute appropriate security skill based on context

  • mcp_tool

    Execute MCP-integrated security tools

  • export_pdf

    Export security findings to PDF format

说明:Tools were inferred from the skill system documentation and feature descriptions. The README mentions MCP tool integration and various security tools but doesn't provide a complete list of MCP tool names with exact signatures. The tools lis

可对比工具

security-tool-mcpai-security-platformmcp-pentest-tools

安装

安装

**要求:** Python 3.9+,Node.js 18+,npm,Docker Desktop

**一键安装(推荐):**

git clone <repository-url>
cd ramibot

# Linux / macOS
bash install.sh

# Windows
install.bat

**手动安装:**

  1. 克隆并导航到仓库
  2. 后端:cd backend; python -m venv .venv; .venv\Scripts\activate (Windows) 或 source .venv/bin/activate (Unix); pip install -r requirements.txt
  3. 前端:cd frontend; npm install
  4. 运行:bash start.sh (Unix) 或 start.bat (Windows)

**设置:** 编辑 backend/settings.json 添加您的API密钥。

ramibot 对比

GitHub →

最后更新于 · 由 README + GitHub 公开数据自动生成。