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公明
86 lines
3.4 KiB
Markdown
86 lines
3.4 KiB
Markdown
# Pent Claude Agent MCP
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[中文](README_CN.md)
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AI-powered **penetration testing engineer** MCP server. CyberStrikeAI can command it to run pentest tasks, analyze vulnerabilities, and perform security diagnostics. The agent runs a Claude-based AI internally and can be configured with its own MCP servers and tools.
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## Tools
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| Tool | Description |
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|------|-------------|
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| `pent_claude_run_pentest_task` | Run a penetration testing task. The agent executes independently and returns results. |
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| `pent_claude_analyze_vulnerability` | Analyze vulnerability information and provide remediation suggestions. |
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| `pent_agent_execute` | Execute a task. The agent chooses appropriate tools and methods. |
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| `pent_agent_diagnose` | Diagnose a target (URL, IP, domain) for security assessment. |
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| `pent_claude_status` | Get the current status of pent_claude_agent. |
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## Requirements
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- Python 3.10+
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- `mcp`, `claude-agent-sdk`, `pyyaml` (included if using the project venv; otherwise: `pip install mcp claude-agent-sdk pyyaml`)
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## Configuration
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The agent uses `pent_claude_agent_config.yaml` in this directory by default. You can override via:
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- `--config /path/to/config.yaml` when starting the MCP server
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- Environment variable `PENT_CLAUDE_AGENT_CONFIG`
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Config options (see `pent_claude_agent_config.yaml`):
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- `cwd`: Working directory for the agent
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- `allowed_tools`: Tools the agent can use (Read, Write, Bash, Grep, Glob, etc.)
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- `mcp_servers`: MCP servers the agent can use (e.g. reverse_shell)
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- `env`: Environment variables (API keys, etc.)
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- `system_prompt`: Role and behavior definition
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Path placeholders: `${PROJECT_ROOT}` = CyberStrikeAI root, `${SCRIPT_DIR}` = this script's directory.
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## Setup in CyberStrikeAI
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1. **Paths**
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Example: project root `/path/to/CyberStrikeAI-main`
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Script: `/path/to/CyberStrikeAI-main/mcp-servers/pent_claude_agent/mcp_pent_claude_agent.py`
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2. **Web UI** → **Settings** → **External MCP** → **Add External MCP**. Paste JSON (replace paths with yours):
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```json
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{
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"pent-claude-agent": {
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"command": "/path/to/CyberStrikeAI-main/venv/bin/python3",
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"args": [
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"/path/to/CyberStrikeAI-main/mcp-servers/pent_claude_agent/mcp_pent_claude_agent.py",
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"--config",
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"/path/to/CyberStrikeAI-main/mcp-servers/pent_claude_agent/pent_claude_agent_config.yaml"
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],
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"description": "Penetration testing engineer: run pentest tasks, analyze vulnerabilities, get status",
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"timeout": 300,
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"external_mcp_enable": true
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}
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}
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```
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- `command`: Prefer the project **venv** Python; or use system `python3`.
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- `args`: **Must be absolute path** to `mcp_pent_claude_agent.py`. Add `--config` and config path if needed.
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- `timeout`: 300 recommended (pentest tasks can be long).
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- Save, then click **Start** for this MCP to use the tools in chat.
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3. **Typical workflow**
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- CyberStrikeAI calls `pent_claude_run_pentest_task("Scan target 192.168.1.1 for open ports")`.
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- pent_claude_agent starts a Claude agent internally, which may use Bash, nmap, etc.
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- Results are returned to CyberStrikeAI.
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## Run locally (optional)
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```bash
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# From project root, with venv
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./venv/bin/python mcp-servers/pent_claude_agent/mcp_pent_claude_agent.py
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```
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The process talks MCP over stdio; CyberStrikeAI starts it the same way when using External MCP.
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## Security
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- Use only in authorized, isolated test environments.
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- API keys in config should be kept secure; prefer environment variables for production.
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