Caldera MCP Plugin

An AI-powered plugin for Caldera that orchestrates long-running LLM workflows to automatically create adversary emulation abilities and plan operations. Optionally enriches workflows with Retrieval-Augmented Generation (RAG) using Cyber Threat Intelligence (CTI) from STIX JSON files. All executions are tracked via MLflow for full observability into LLM reasoning and tool usage.

Features

• LLM Ability Factory: Generate custom Caldera abilities from natural language descriptions
• LLM Operation Planner: Create and execute complete adversary emulation operations
• CTI Integration: Enhance abilities with real-world threat intelligence from STIX bundles
• MLflow Tracking: Full observability of LLM reasoning, tool calls, and execution trajectory
• Flexible Model Support: Works with most LLM providers (OpenAI, Anthropic, etc.)
• Run History: Browse and search all historical executions with full details

Quick Start

1. Start Caldera

From the Caldera root directory:

python3 server.py --insecure

The MCP plugin automatically starts MLflow on port 5000 during Caldera initialization.

2. Access the MCP Interface

Navigate to the Caldera web interface and select the MCP plugin from the sidebar.

3. Configure Your LLM

In the Global Model Configuration panel:

• Enter your API key (required)
• Select your model (default: gpt-4o)
• Adjust temperature and max_tokens as needed
• Set max tool calls for ReAct iterations (default: 5)

4. Choose Your Workflow

LLM Ability Factory: Create specific abilities

• Example: “Create a Windows ability that dumps credentials using PowerShell”

LLM Operation Planner: Plan and execute operations

• Example: “Execute a ransomware simulation on Windows agents”

5. Run Your Task

1. Enter your prompt in natural language
2. (Optional) Select STIX CTI files to enhance with threat intelligence
3. Click Execute
4. Watch real-time progress via MLflow stages and reasoning
5. View results and created abilities/operations

Architecture

Core Components

Frontend (Vue.js)

• mcp.vue: Main landing page with navigation
• local_mcp_ability_factory.vue: Ability creation interface
• public_mcp_ability_factory.vue: Public ability interface
• mcp_history.vue: Historical run browser
• mcp_extension_guide.vue: Developer extension guide

Backend (Python)

• mcp_api.py: aiohttp API routes
• mcp_svc.py: Service orchestration layer
• mcp_factory_client.py: Ability factory DSPy client
• mcp_planner_client.py: Operation planner DSPy client
• mcp_server.py: MCP tool server exposing Caldera API
• factory.py: Command generation DSPy module
• rag.py: STIX CTI retrieval service

Integration

• hook.py: Plugin initialization and MLflow startup

Configuration

Default Settings

Edit conf/default.yml to set default LLM configuration:

llm:
  model: gpt-4o
  api_key: YOUR_API_KEY
  offline: true
  use_mock: false
factory:
  model: gpt-4o
  api_key: YOUR_API_KEY
  temperature: 0.4

Note: Frontend model configuration overrides these defaults per run.

RAG Configuration

When using CTI enhancement:

• Embedding Model: Default openai/text-embedding-3-small
• Top-K Retrieval: Default 5 objects (configurable via UI)
• STIX File Location: Upload files via UI, stored in data/ directory

Using CTI Integration

Upload STIX Files

1. Navigate to MCP → Ability Factory or Planner
2. In the RAG Configuration section, click Upload STIX File
3. Select your STIX JSON bundle(s)
4. Files are stored in plugins/mcp/data/

Enable CTI for a Run

1. In the RAG Configuration panel, select which STIX files to use
2. (Optional) Adjust embedding model and top-K retrieval
3. Execute your task normally

The LLM will receive relevant CTI context based on your prompt, including:

• Attack patterns and techniques
• Malware and tool descriptions
• Threat actor TTPs
• Campaign information

CTI Data Flow

User Prompt → RAG Search (Semantic) → Top-K CTI Objects Retrieved
                                    ↓
                        Detailed Context for Top 3 Objects
                                    ↓
                        Formatted CTI Context String
                                    ↓
                        LLM Receives Task + CTI Context
                                    ↓
                        Creates CTI-Informed Abilities/Operations

MLflow Tracking

Access MLflow UI

Open your browser to: http://localhost:5000

Navigate to Experiments → Traces to view:

• Run status and stages
• LLM chain of thought (thought_0, thought_1, etc.)
• Tool calls and arguments (tool_name_N, tool_args_N)
• RAG retrieval steps (when CTI is used)
• Final results and reasoning

Understanding Run Tags

Status Tags:

• status: running, complete, failed
• stage: Current execution phase
• reasoning: LLM’s final reasoning summary
• process_result: Summary of what was created

RAG Tags (when CTI is enabled):

• rag_retrieval_step_N: RAG retrieval process
• rag_retrieved_object_N: Names of CTI objects retrieved
• cti_context_preview: First 1000 chars of CTI sent to LLM
• cti_context_length: Total CTI context size

LLM Trajectory:

• thought_N: LLM reasoning at each step
• observation_N: Tool execution results
• tool_name_N: Tool that was called
• tool_args_N: Arguments passed to tool

Extending the Framework

See the Extend & Customize guide in the UI for detailed instructions on:

• Creating custom DSPy clients
• Adding new MCP tools
• Building custom workflows
• Integrating with the service layer

Example use cases:

• Threat Hunter: Analyze adversary profiles and generate detection rules
• Operation Optimizer: Review completed operations and suggest improvements
• Campaign Builder: Create multi-stage campaigns from threat actor profiles

Development

Testing Individual Components

# Test factory client (requires running Caldera)
cd app
python mcp_factory_client.py

# Test planner client
python mcp_planner_client.py

# Test MCP server tools
python mcp_server.py

Project Structure

plugins/mcp/
├── app/                    # Python backend
│   ├── mcp_api.py         # API routes
│   ├── mcp_svc.py         # Service layer
│   ├── mcp_factory_client.py
│   ├── mcp_planner_client.py
│   ├── mcp_server.py      # MCP tool server
│   ├── factory.py         # Command generation
│   └── rag.py             # CTI retrieval
├── gui/views/             # Vue frontend
│   ├── mcp.vue
│   ├── local_mcp_ability_factory.vue
│   ├── public_mcp_ability_factory.vue
│   ├── mcp_history.vue
│   └── mcp_extension_guide.vue
├── conf/default.yml       # Default configuration
├── data/                  # STIX JSON files
├── hook.py                # Plugin initialization
└── README.md

Dependencies

• dspy: LLM orchestration framework with ReAct pattern
• mcp: Model Context Protocol SDK for tool servers
• mlflow: Experiment tracking and tracing
• aiohttp: Async web framework
• psutil: Process management for MLflow server
• requests: HTTP client for Caldera API

Troubleshooting

API Key Errors

Error: “API key is required but not provided”

Solution: Enter your API key in the Global Model Configuration panel before executing.

MLflow Not Starting

Error: Cannot access http://localhost:5000

Solution: Check Caldera logs for MLflow startup messages. The plugin automatically kills processes on port 5000 and starts MLflow during initialization.

RAG Retrieval Failures

Error: “RAG service not initialized” or embedding errors

Solution:

• Verify STIX files are valid JSON
• Ensure API key has access to embedding models
• Check MLflow logs for detailed error messages

Tool Execution Failures

Error: Tools fail to initialize or execute

Solution:

• Verify Caldera API is accessible at http://localhost:8888/api/v2/
• Check MCP server subprocess logs for environment issues
• Ensure PYTHONPATH includes venv packages

Viewing Detailed Logs

Check MLflow UI for:

1. Full trajectory of tool calls
2. Error messages in error param
3. Traceback in traceback param
4. Stage where failure occurred

Support

For bugs and feature requests:

• Check MLflow traces for detailed execution information
• Review Caldera logs with [MCP] prefix
• Consult the in-app Extension Guide for development questions

License

Part of the Caldera project. See main Caldera repository for license information.