Kali Security MCP
Provides intelligent security testing capabilities by integrating 193 Kali Linux security tools including information gathering, web application testing, password attacks, vulnerability exploitation, and PWN/reverse engineering tools with AI-driven decision making.
Enables exploitation framework operations through automated vulnerability exploitation and attack execution capabilities.
Implements OWASP security testing methodologies for comprehensive web application security assessment and vulnerability scanning.
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Here is a step-by-step guide with screenshots.
Kali MCP — 多智能体渗透测试集群
确定性编排的 17-agent 渗透测试集群
中文
定位
Kali MCP 是一个确定性的多智能体渗透测试集群:通过 MCP 协议或命令行,把「对某目标做渗透测试」这一自然语言任务,自动分解为侦察、扫描、利用、验证等子任务,调度 1 个协调器 + 17 个专业智能体并发执行真实工具,最终聚合出带严重性分级的结构化报告。
架构
flowchart TB
U[自然语言任务] --> IA[IntentAnalyzer 意图分析]
IA --> TD[TaskDecomposer 任务分解<br/>攻击面测绘/内容发现/漏洞假设/验证固化]
TD --> SCH[AgentScheduler 波次并发调度<br/>依赖满足即并发,跨阶段保序]
SCH --> A[17 个专业 Agent]
A --> RA[ResultAggregator 结果聚合<br/>否定上下文门控,消除 clean 误报]
RA --> R[结构化报告 + findings]17 个专业智能体
分组 | 智能体 | 职责 |
信息收集 |
| 端口扫描 / 服务识别 / OS 指纹 / 拓扑侦察 |
| 子域名枚举 / DNS 记录 / OSINT | |
| 目录枚举 / 技术栈识别 / WAF 检测 / CMS 指纹 | |
漏洞发现 |
| CVE / 模板化漏洞扫描 |
| SQLi / XSS / 命令注入等 Web 漏洞 | |
| 在线爆破 / 哈希破解 / 凭据喷洒 | |
| SMB 枚举 / LLMNR 投毒 / MITM / 嗅探 | |
| 候选漏洞验证 / PoC 构造 / 利用确认 | |
利用 |
| Metasploit / exploit 搜索 / 反弹 shell |
| Linux / Windows 提权向量分析 | |
| DCSync / Kerberoast / AD 攻击 / 凭据重用 | |
专门 |
| 白盒源码树扫描 / 危险模式分析 |
| SAST 静态分析 / 危险模式搜索 | |
| CTF 密码学 / 编码识别 / 哈希破解 | |
| 隐写 / 内存取证 / 文件系统取证 / 流量分析 | |
| 二进制漏洞检查 / 逆向 / 反编译 | |
| .git/.svn 泄露 / 备份扫描 / LFI 读源码 |
协调器 CoordinatorAgent 承接 report_generator 与无可用智能体的兜底任务。
核心特性
特性 | 说明 |
确定性编排 | 意图分析 / 分解 / 调度 / 聚合全链路算法化,无 LLM 密钥可用 |
波次并发调度 | 依赖满足即并发( |
自研 fastsec 引擎 | Go 重写,替代 25 个传统工具(nmap/sqlmap/nuclei/gobuster 等),单二进制全模式 |
知识库优先 | HackReport 经验库 + 263 万口令 / 667 万子域 / 6.3 万 payload 字典 |
实时可视化 |
|
多后端执行 | local / ssh / docker 动态解析,本地与远程 Kali 无缝切换 |
Session 生命周期 | TTL 自动过期(4h),后台清理防内存泄漏 |
快速开始
方式一:MCP(自然语言编排)
在支持 MCP 的客户端(Claude Desktop / Claude Code / Oh My Pi)配置:
{
"mcpServers": {
"kali": {
"command": "python",
"args": ["mcp_server.py", "--tool-profile", "harness"],
"env": {
"KALI_MCP_FORCE_ENABLE_MODULES": "multi_agent",
"K4_LEGACY_CLUSTER": "1"
}
}
}
}harness 档位暴露 13 个编排工具,核心三入口:
工具 | 用途 |
| 自然语言任务 → 集群全流程(意图→分解→调度→聚合) |
| 集群健康 / agent 列表 / 调度统计 |
| 单工具兜底(任意注册工具名) |
另有 start_task / run_surface_chain(surface playbook 顺序执行)/ board_snapshot / task_* / verify_finding 等任务板与验证工具。
方式二:CLI 实时可视化
# Windows
C:/Windows/py.exe -3 agent_live.py "对 http://localhost:8000/ 做 web 漏洞扫描:目录枚举、CMS识别、注入检测" --no-cache --timeout 300
# Linux / macOS
python3 agent_live.py "对 http://localhost:8000/ 做 web 漏洞扫描" --agents recon,web_vuln --no-cacheagent_live.py 逐行分色打印每个智能体的调度决策与工具调用结果,Windows Terminal 分屏即可获得类 tmux 的实时观察体验。
编排流水线
一条任务经过四阶段确定性编排(以 Web 目标为例):
攻击面测绘:
nmap/masscan/subfinder/amass→ recon×2 + subdomain×2内容发现:
gobuster/dirb/ffuf/feroxbuster→ web_recon×4漏洞假设:
nuclei_web/nuclei/nikto/wpscan→ vuln_scanner×4验证固化:
sqlmap/intelligent_xss_payloads/ 命令注入深挖 → web_vuln×4
surface playbook(kali_mcp/core/playbooks/)提供 web_surface / api_surface / auth_surface / svc_surface / internal_lateral / stealth / chain / ai_guided 八类标准打法,run_surface_chain 可顺序执行。
实测数据(2026-08-13 回归)
指标 | 结果 |
冷跑总耗时 | 174.76s(< 300s 硬超时,波次并发 + |
会话状态 | completed,17/17 任务完成,failed 0 |
调度成功率 | 16/16(100%) |
findings 误报 | XSS clean 误报已修复,仅 1 个真实 SQL 注入 high finding |
后端执行
执行后端由 resolve_backend() 启动时动态解析:本地 PATH 有 Kali 工具走 local;检测到 SSH 配置走 ssh 后端(paramiko 免密);容器环境走 docker。无需硬编码远程地址。
合规声明
本项目仅用于已获书面授权的渗透测试、CTF 竞赛、安全研究与防御性评估。使用前通过 set_engagement_context 声明授权范围;越权扫描、破坏性操作、未授权攻击严格禁止。
License
MIT License — 详见 LICENSE。
Related MCP server: MCP Kali Server
English
Positioning
Kali MCP is a deterministic multi-agent penetration testing cluster: one natural-language instruction is decomposed into recon, scanning, exploitation, and verification subtasks, dispatched across 1 coordinator + 17 specialized agents running real tools concurrently, and aggregated into a severity-graded structured report.
The full pipeline — intent analysis → task decomposition → scheduling → aggregation — is deterministic and runs without any LLM API key; LLM keys only enable optional deep-reasoning enhancement.
Architecture
instruction → IntentAnalyzer → TaskDecomposer → AgentScheduler (wave-concurrent)
→ 17 agents → ResultAggregator (negation-gated) → structured report17 Specialized Agents
Information gathering:
recon_agent,subdomain_agent,web_recon_agentVulnerability discovery:
vuln_scanner_agent,web_vuln_agent,auth_agent,network_vuln_agent,vuln_verifier_agentExploitation:
exploit_agent,privilege_agent,lateral_agentSpecialized:
code_analyze_agent,code_audit_agent,crypto_agent,forensics_agent,pwn_agent,source_code_agent
Quick Start
{
"mcpServers": {
"kali": {
"command": "python",
"args": ["mcp_server.py", "--tool-profile", "harness"],
"env": {
"KALI_MCP_FORCE_ENABLE_MODULES": "multi_agent",
"K4_LEGACY_CLUSTER": "1"
}
}
}
}Three core MCP entries: agent_run (full pipeline), agent_status (cluster health), kali_run (single-tool fallback).
For real-time visualization:
python3 agent_live.py "web vuln scan on http://localhost:8000/" --no-cacheMeasured (2026-08-13 regression)
Cold run: 174.76s (under 300s cap, wave-concurrency +
-c 50)Session: completed, 17/17 tasks, 0 failures
Scheduling success: 100% (16/16)
Findings: 1 real high-severity SQL injection, XSS clean false-positive eliminated
Disclaimer
For authorized security testing only. Users are responsible for complying with all applicable laws and regulations.
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