
一款由 Caldera 开发的 AI 插件,可协调长期运行的大型语言模型工作流,自动创建对抗者仿真能力并规划操作。
概览
一款由 Caldera 开发的 AI 插件,可协调长期运行的大型语言模型工作流,自动创建对抗者仿真能力并规划操作。可选地通过网络威胁情报(CTI)从 STIX JSON 文件中丰富工作流程。所有执行均通过机器学习流跟踪,实现对 LLM 推理和工具使用的完整可观察性。 - : 从自然语言描述生成自定义Caldera能力 - : 创建并执行完整的对手仿真操作 - : 通过STIX捆绑包的真实威胁情报增强能力 - : 对LLM推理、工具调用和执行轨迹的全面可观察性 - : 支持大多数LLM提供商(如OpenAI、Anthropic等) - : 浏览和搜索所有历史执行及详细信息 MCP 插件在 Caldera 初始化时会自动启动 MLflow,连接端口 5000。 进入Caldera网页界面,从侧边栏选择插件。
概述(AI 翻译)
CALDERA 的 AI 辅助运营工作空间。聊天驱动的工作流程创建对手内容,将 CTI 导入 STIX,并规划和运行对手仿真,LLM 基于 MCP 工具和实时 CALDERA 状态,而非操作员复制粘贴。
工作流程
| 工作流程 | 它的作用 |
|---|---|
| 作者 | 通过自然语言提示创建或更新CALDERA的能力和敌人。 |
| 上传CTI | 导入HTML、PDF、TXT或Markdown报告,并生成STIX 2.1捆绑包。 |
| 计划并执行 | 选择STIX捆绑包,将报告融合到一个参与者身上,从观察到的技术构建对手,运行操作,并报告覆盖差距。 |
在“计划与执行”中选择STIX可启用该次运行的CTI检索;若未选中,工作流程则在不选CTI的情况下运行。每次运行都会保留其转录记录、工具调用和工件以供审查。
管道生产的
威胁报告告诉你该跑什么。你的环境告诉你在哪里。
提取只得到 ATT&CK 技术、命名的威胁行为者和文件哈希可观测量,除此之外什么都没有。
本页由 AI 翻译,技术细节请以原文为准。
README
以下为原文 README 节选,完整内容请查看原文。
Caldera MCP Plugin
An AI-assisted operations workspace for CALDERA. Chat-driven workflows create adversary content, ingest CTI into STIX, and plan and run adversary emulation, with the LLM grounded in MCP tools and live CALDERA state instead of operator copy-paste.
Workflows
| Workflow | What it does |
|---|---|
| Author | Creates or updates CALDERA abilities and adversaries from a natural-language prompt. |
| Upload CTI | Ingests HTML, PDF, TXT or Markdown reports and produces STIX 2.1 bundles. |
| Plan and Execute | Selects STIX bundles, fuses reports on one actor, builds an adversary from the observed techniques, runs the operation, and reports coverage gaps. |
Selecting STIX in Plan and Execute enables CTI retrieval for that run; with none selected the workflow runs without it. Every run keeps its transcript, tool calls and artifacts for review.
What the pipeline produces
A threat report tells you what to run. Your environment tells you where.
Extraction yields ATT&CK techniques, the named threat actor and file hash observables, and nothing else. Hosts, accounts, domains, services and software describe the report’s victim rather than your estate, so they never become CALDERA facts. CALDERA discovers those at runtime, which is why the abilities that use them carry has_agent_copy and no_backwards_movement requirements.
The one insertion point into core CALDERA is the adversary profile:
upload report(s) -> clean -> IR -> ATT&CK techniques -> STIX bundle
|
"build an adversary from this and run it" v
build_adversary attack-pattern -> ability.technique_id -> Adversary
+ unmatched_techniques + platform_excluded
run_operation Adversary + your agents + runtime-discovered facts
Installation
Install like any CALDERA plugin, from the CALDERA root:
git clone https://github.com/mitre/mcp.git plugins/mcp
source venv/bin/activate
pip install -r plugins/mcp/requirements.txt
python -m spacy download en_core_web_lg
cp plugins/mcp/.env.example plugins/mcp/.env
The spaCy model needs its own command because pip cannot install it from requirements.txt. Then add mcp to the plugins: list in conf/local.yml, alongside magma, sandcat and stockpile. Do not commit that file.
Optional extras:
brew install poppler # macOS, supplies pdftotext
apt install poppler-utils # Debian/Ubuntu
pip install -r plugins/mcp/requirements-dev.txt # to run the test suite
Without poppler, TXT, MD and HTML still ingest and PDF uploads report the missing dependency rather than failing silently.
LLM credentials
| Variable | Purpose |
|---|
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →