by asaotomo · MCP Server · ★ 699
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FofaMap safe to install? View the security audit →
🗺️ FofaMap v2.0 — Platinum Agent Edition 🧠 全网首个支持 MCP 协议 + AI 自我反思机制的红队资产测绘智能体 Self-Reflecting AI Queries → MCP Protocol Integration → Intelligent Vulnerability Scanning ✨ 一句话介绍 ❌ 它不是 FOFA 工具 ❌ 也不是 Nuclei 封装 ✅ 它是：一个可以被 AI 接管、会自己反思、会自己决策扫描策略的「全网资产测绘智能体」 目前fofamapv2.0版本已作为 Skill 上线 ClawHub 技能商城，您可通过以下命令安装，让您的 AI 助手（“龙虾”）也能使用资产测绘： 🚀 v2.0 是什么级别的升级？ FofaMap 从「资产查询工具」→ 进化为 AI Agent（智能体） 🧠 1️⃣ 你只要说人话 AI 自动完成： 理解你的意图 生成 FOFA 语法 查询资产 若 0 结果 → 启动 自我反思机制 自动放宽条件重试 分析资产指纹 判断是否值得扫描 自动生成 Nuclei 扫描参数 询问你是否执行 🤖 2️⃣ 会“自我反思”的 AI（核心黑科技） 不再出现： AI 会自动： 判断： 是否地区条件过严？ 是否关键词不合理？ 是否语法太死？ 自动： 放宽条件 重写语法 多策略重试 直到：尽量给你产出结果 🔌 3️⃣ 原生 MCP 支持（给 AI
git clone https://github.com/asaotomo/FofaMap.gitTaken from the project's README on GitHub. Check the security audit before running it.
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 699 |
| Forks | 93 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 61.0294057715335/100 |
| Open Issues | 31 |
| Last Updated | 2026-08-16 |
| Created | 2021-12-29 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~17k |
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FofaMap is 一款证据驱动的 FOFA 资产测绘智能体：支持自然语言侦察、AI 反思、CLI / MCP / Skill / REST API，以及经人工审批的 Nuclei 扫描。. It is categorized as a MCP Server with 699 GitHub stars.
FofaMap is primarily written in Python. It covers topics such as ai-agent, asset-discovery, asset-mapping.
The README on GitHub (github.com/asaotomo/FofaMap) gives: git clone https://github.com/asaotomo/FofaMap.git. Usage details are in the same README.
FofaMap is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to FofaMap on Agent Skills Hub include redamon, CyberStrike, ARL-Next. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: