What is Offensive Mcp Ai

Offensive-MCP-AI is an AI-driven security automation platform designed for threat detection and response, utilizing the Model Context Protocol (MCP) to enhance cybersecurity operations.

Use cases

Use cases include automating reconnaissance and exploitation tasks, real-time incident analysis and response recommendations, malware development and evasion techniques, and providing cybersecurity training and simulations.

How to use

To use Offensive-MCP-AI, install the MCP CLI and SDK via pip, configure the Claude desktop application, and integrate various security tools and logs for automated analysis and incident response.

Key features

Key features include autonomous red team agents, AI-powered SOC analyst capabilities, malware development automation, threat hunting automation, incident report generation, and cybersecurity training simulations.

Where to use

Offensive-MCP-AI can be utilized in cybersecurity operations, incident response teams, threat intelligence analysis, and security training environments across various industries.

Offensive-MCP-AI

🔮 Future Work Using MCP and AI

1. Autonomous Red Team Agents
   Build LLM-driven agents that autonomously conduct reconnaissance, payload generation, exploitation and reporting, all orchestrated via MCP tools.

2. AI-Powered SOC Analyst
   Integrate Wazuh + Suricata + Zeek logs and use MCP to let Claude analyze incidents, detect lateral movement, and recommend response actions in real-time.

3. Malware Dev Studio (LLM + MCP)
   Use Claude + MCP to automate shellcode generation, obfuscation, sandbox evasion, and EDR bypass strategies through tools like Capstone, Donut, and Sliver.

4. Threat Hunting Automation
   Develop proactive AI workflows that analyze logs, correlate indicators, and hunt based on threat intelligence feeds via MCP resources and tools.

5. Agent-Based Purple Team Simulator
   Combine MCP with ATT&CK simulations, where Claude orchestrates both Red and Blue side techniques (Atomic Red Team, Caldera, Sigma/YARA rule generation).

6. CI/CD + DevSecOps Integration
   Use MCP to review code pushed to GitHub, scan secrets, trigger security tools (Trufflehog, Gitleaks), and send secure alerts or PR recommendations.

7. Auto Incident Report Generator
   Claude consumes logs and tool outputs via MCP and generates full incident reports (including diagrams and mitigations) in Markdown or PDF formats.

8. Cybersecurity Tutor / Trainer Mode
   Claude explains what each tool does, simulates attacks in safe lab environments, and evaluates user responses via MCP simulation tools.

🔗 Installation & Integration Links

✅ Install MCP CLI and SDK (Python)

pip install modelcontextprotocol

Docs:
🔗 https://modelcontextprotocol.io/quickstart/server
GitHub:
🔗 https://github.com/jlowin/fastmcp

🧠 Claude Desktop Configuration (Mac, Linux, Windows)

1. Install Claude for Desktop
   🔗 https://www.anthropic.com/index/claude-desktop

2. Edit config file:

macOS/Linux

nano ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows

notepad %AppData%\Claude\claude_desktop_config.json

3. Add your MCP server:

{
  "mcpServers": {
    "my-wazuh-agent": {
      "command": "/full/path/to/python",
      "args": [
        "mcp_wazuh_server.py"
      ]
    }
  }
}

4. Restart Claude Desktop — you’ll see the connector icon (⚡) for prompts and the tools icon (🛠) for tool invocation.

🧪 Test Locally with Inspector

Run your server with debugging:

npx @modelcontextprotocol/inspector python mcp_wazuh_server.py

This opens a local UI where you can test @mcp.tool() and @mcp.prompt() before linking with Claude.