MC 미래 작업 사용 MCP 및 AI

1. ** 자율 레드 팀 에이전트 ** 자율적으로 reconnaissance, 페이로드 세대, 악용 및보고, MCP 도구를 통해 모든 오케스트라를 통해 LLM 구동 에이전트 구축.

2. **AI 전원 SOC 분석 ** Wazuh + Suricata + Zeek 로그와 MCP를 사용하여 Claude 분석 사건을 분석하고 측면 운동을 감지하고 실시간 응답 작업을 권장합니다.

3. **Malware Dev Studio (LLM + MCP) ** Claude + MCP를 사용하여 Capstone, Donut 및 Sliver와 같은 도구를 통해 쉘 코드 생성, 손상, 샌드 박스 증발 및 EDR 바이패스 전략을 자동화합니다.

4. **Threat Hunting 자동화 ** MCP resources 및 tools를 통해 위협 인텔리전스 피드를 기반으로 로그, correlate 지표를 분석하는 유능한 AI 워크플로우 개발.

5. **Agent 기반 퍼플 팀 시뮬레이터 ** ATT&CK 시뮬레이션을 가진 MCP를 결합하여 Red와 Blue side 기술을 모두 관현악하는 곳(Atomic Red Team, Caldera, Sigma/YARA 규칙 생성).

6. **CI/CD + DevSecOps 통합 ** MCP를 사용하여 GitHub, 스캔 비밀, 방아쇠 보안 도구 (Trufflehog, Gitleaks)에 푸시된 코드를 검토하고 안전한 경고 또는 PR 권고를 보냅니다.

7. 명세.

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.

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🔗 Installation & Integration Links

✅ Install MCP CLI and SDK (Python)

pip install modelcontextprotocol

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

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🧠 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.

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🧪 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.