ThreatByte-MCP
ThreatByte-MCP is a deliberately vulnerable, MCP-based case management web app. It mirrors a realistic SOC analyst workflow with a server-rendered UI and a real MCP server. The MCP tools are intentionally vulnerable for training and demonstration.
ThreatByte-MCP
ThreatByte-MCP is a deliberately vulnerable, MCP-based case management web app. It mirrors a realistic SOC analyst workflow with a server-rendered UI and a real MCP server. The MCP tools are intentionally vulnerable for training and demonstration.
[!NOTE] For educational use in controlled environments only.
Features
- Safe web authentication (signup/login/logout)
- Case management UI (create/list/view cases)
- Notes and attachments tied to cases
- Indicator search and agent workflows via MCP tools
- Agent customization with schema-based tool registry
MCP Server (SDK, JSON-RPC)
ThreatByte-MCP is a split architecture:
- SOC Web App (client/UI) runs on port 5001.
- MCP Server (tools + agent) runs on port 5002 using the official MCP Python SDK (FastMCP).
The MCP server exposes JSON-RPC at POST http://localhost:5002/mcp (Streamable HTTP). The web UI calls the MCP server through a server-side proxy to keep auth consistent with the SOC session; the proxy streams agent responses to the browser via SSE. A sample mcp.json manifest is included at the repo root.
All direct MCP calls must include MCP-Protocol-Version: 2025-11-25 and Accept: application/json, text/event-stream.
Architecture (simplified):
Browser
|
v
+------------------+ X-TBMCP-Token + X-TBMCP-User +-------------------+
| SOC Web App | ---------------------------------------> | MCP Server |
| (Flask, :5001) | /mcp-proxy (server-side) | (FastMCP, :5002) |
+------------------+ +-------------------+
| |
v v
SQLite DB Tool registry
Agent + tool handlers
Architecture (detailed):
Browser (Analyst)
|
v
SOC Web App (Flask, :5001)
| - Auth session (cookie)
| - Dashboards, cases, notes, files UI
| - /mcp-proxy forwards JSON-RPC
|
+--> SQLite DB
| - users, cases, notes, files, indicators
|
+--> Uploads (app/uploads)
|
v
MCP Server (FastMCP, :5002)
| - /mcp JSON-RPC (Streamable HTTP)
| - X-TBMCP-Token + X-TBMCP-User headers
|
+--> Tool registry (mcp_tools)
| - schema-based tools (poisonable)
|
+--> Agent runtime
| - prompt builder (hardcoded tokens)
| - LLM API call
|
+--> Persistence
- agent_contexts (prompt store)
- agent_logs (full request/response)
MCP Auth Between Web App and MCP Server
The web app proxies MCP calls with these headers:
X-TBMCP-Token: shared secret fromTBMCP_MCP_SERVER_TOKEN(configured on both servers).X-TBMCP-User: current user id from the authenticated SOC session.
Direct MCP calls require the same headers.
Supported tools:
cases.createcases.listcases.list_allcases.getcases.renamecases.set_statuscases.deletenotes.createnotes.listnotes.updatenotes.deletefiles.upload(base64)files.listfiles.get(base64)files.read_pathindicators.searchagent.summarize_caseagent.run_tasktools.registry.listtools.builtin.listtools.registry.registertools.registry.delete
Vulnerability Themes (Training-Focused)
The following weaknesses are intentionally present for teaching:
- Broken object level authorization (cases/notes/files, list_all)
- Stored XSS (notes rendered as trusted HTML)
- SQL injection in indicator search
- Prompt injection in agent task runner
- Token mismanagement & secret exposure (hardcoded tokens in prompts, persisted contexts, full logs)
- Tool poisoning via schema-driven tool registry overrides (MCP03)
- Over-trusting client context (MCP header identity spoofing)
- Arbitrary file read via
files.read_path - Cross-user file overwrite (shared filename namespace)
Running Locally
cd ThreatByte-MCP
python -m venv venv_threatbyte_mcp
source venv_threatbyte_mcp/bin/activate
pip install -r requirements.txt
python db/create_db_tables.py
python run_http_server.py
python run.py
Open: http://localhost:5001
MCP Server: http://localhost:5002/mcp
Running with Docker or Podman
The repository includes a Dockerfile and startup script that initialize the DB and run both services in one container:
- SOC Web App on
:5001 - MCP Server on
:5002
Build the image:
# Docker
docker build -t threatbyte-mcp .
# Podman
podman build -t threatbyte-mcp .
Run the container:
# Docker
docker run --rm -p 5001:5001 -p 5002:5002 threatbyte-mcp
# Podman
podman run --rm -p 5001:5001 -p 5002:5002 threatbyte-mcp
Run with optional environment variables:
# Docker
docker run --rm -p 5001:5001 -p 5002:5002 \
-e TBMCP_MCP_SERVER_TOKEN=tbmcp-mcp-token \
-e OPENAI_API_KEY=your_api_key \
-e TBMCP_OPENAI_MODEL=gpt-4o-mini \
threatbyte-mcp
# Podman
podman run --rm -p 5001:5001 -p 5002:5002 \
-e TBMCP_MCP_SERVER_TOKEN=tbmcp-mcp-token \
-e OPENAI_API_KEY=your_api_key \
-e TBMCP_OPENAI_MODEL=gpt-4o-mini \
threatbyte-mcp
Persist SQLite data between runs (optional):
# Docker
docker run --rm -p 5001:5001 -p 5002:5002 \
-v "$(pwd)/db:/app/db" \
-v "$(pwd)/app/uploads:/app/app/uploads" \
threatbyte-mcp
# Podman
podman run --rm -p 5001:5001 -p 5002:5002 \
-v "$(pwd)/db:/app/db:Z" \
-v "$(pwd)/app/uploads:/app/app/uploads:Z" \
threatbyte-mcp
Populate Sample Data
python db/populate_db.py --users 8 --cases 20 --notes 40 --files 20
This creates random users, cases, notes, and file artifacts. All user passwords are Password123!.
LLM Integration (Required for Agent Responses)
The agent task endpoint requires a real LLM. Without an API key, the agent returns an error indicating it is unavailable.
Environment variables:
TBMCP_OPENAI_API_KEYorOPENAI_API_KEYTBMCP_OPENAI_MODEL(default:gpt-4o-mini)
Keep API keys server-side only and never expose them in the browser.
MCP Server Configuration
The SOC web app proxies MCP calls to the MCP server using a shared token.
Environment variables:
TBMCP_MCP_SERVER_URL(default:http://localhost:5002/mcp)TBMCP_MCP_SERVER_TOKEN(shared secret between the SOC app and MCP server)
Notes
- The UI uses server-rendered templates.
- MCP tools are exposed under
http://localhost:5002/mcp(JSON-RPC). The UI calls them through/mcp-proxy. - This app is intentionally insecure. Do not deploy it to the public internet.
เซิร์ฟเวอร์ที่เกี่ยวข้อง
mcp-datadog-server
Datadog MCP Server
rfcxml-mcp
MCP server for structural understanding of RFC documents.
ATOM Pricing Intelligence
The Global Price Benchmark for AI Inference. 1,600+ SKUs, 40+ vendors, 25 AIPI indexes.
FHIR MCP Server
FHIR MCP Server – helping you expose any FHIR Server or API as a MCP Server.
Image Reader
A server for extracting and understanding content from images.
Mureka
generate lyrics, song and background music(instrumental)
Vintage Chocolate Recipes (1914)
146 historic chocolate recipes from 1914. Search cakes, candies, and beverages from Maria Parloa's classic cookbook.
Policy Layer
Non-custodial spending controls for AI agent wallets — enforce limits, allowlists, and kill switches before transactions execute.
Weather API MCP Server
Provides current weather data and forecasts using the QWeather API.
Stock Market Tracker
MCP server for advanced financial analysis, stock monitoring, and real-time market intelligence to support buy/sell decisions