Damn Vulnerable Model Context Protocol (DVMCP)
LocalAbout Damn Vulnerable Model Context Protocol (DVMCP)
Damn Vulnerable Model Context Protocol (DVMCP) is a deliberately vulnerable MCP implementation built for education. It contains 10 challenges of increasing difficulty, aimed at security researchers, developers and AI safety professionals.
Key features
- 0110 challenges of increasing difficulty
- 02Covers prompt injection, tool poisoning and related risks
- 03Challenge descriptions and solution guides
- 04Docker-based run environment
- 05MIT licensed
Use cases
- 01Learning common security issues in MCP implementations
- 02Demonstrating attack vectors in security training
- 03Understanding mitigations for improving one's own MCP servers
MCP support
DVMCP is itself a deliberately vulnerable MCP server that exposes tools, resources and prompts to MCP clients to demonstrate attacks.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEDamn Vulnerable Model Context Protocol (DVMCP)
A deliberately vulnerable implementation of the Model Context Protocol (MCP) for educational purposes.
Overview
The Damn Vulnerable Model Context Protocol (DVMCP) is an educational project designed to demonstrate security vulnerabilities in MCP implementations. It contains 10 challenges of increasing difficulty that showcase different types of vulnerabilities and attack vectors.
This project is intended for security researchers, developers, and AI safety professionals to learn about potential security issues in MCP implementations and how to mitigate them.
What is MCP?
The Model Context Protocol (MCP) is a standardized protocol that allows applications to provide context for Large Language Models (LLMs) in a structured way. It separates the concerns of providing context from the actual LLM interaction, enabling applications to expose resources, tools, and prompts to LLMs.
Recommended MCP Clients
CLINE - VSCode Extension
Refer to this Connecting to a Remote Server - Cline for connecting Cline with MCP server
Quick Start
Once you have cloned the repository, run the following commands:
docker build -t dvmcp .
docker run -p 9001-9010:9001-9010 dvmcp
Disclaimer
It's not stable in a Windows environment. If you don't want to use Docker then please use Linux environment. I recommend Docker to run the LAB and I am 100% percent sure it works well in the Docker environment
Security Risks
While MCP provides many benefits, it also introduces new security considerations. This project demonstrates various vulnerabilities that can occur in MCP implementations, including:
- Prompt Injection: Manipulating LLM behavior through malicious inputs
- Tool Poisoning: Hiding malicious instructions in tool descriptions
- Excessive Permissions: Exploiting overly permissive tool access
- Rug Pull Attacks: Exploiting tool definition mutations
- Tool Shadowing: Overriding legitimate tools with malicious ones
- Indirect Prompt Injection: Injecting instructions through data sources
- Token Theft: Exploiting insecure token storage
- Malicious Code Execution: Executing arbitrary code through vulnerable tools
- Remote Access Control: Gaining unauthorized system access
- Multi-Vector Attacks: Combining multiple vulnerabilities
Project Structure
damn-vulnerable-MCP-server/
├── README.md # Project overview
├── requirements.txt # Python dependencies
├── challenges/ # Challenge implementations
│ ├── easy/ # Easy difficulty challenges (1-3)
│ │ ├── challenge1/ # Basic Prompt Injection
│ │ ├── challenge2/ # Tool Poisoning
│ │ └── challenge3/ # Excessive Permission Scope
│ ├── medium/ # Medium difficulty challenges (4-7)
│ │ ├── challenge4/ # Rug Pull Attack
│ │ ├── challenge5/ # Tool Shadowing
│ │ ├── challenge6/ # Indirect Prompt Injection
│ │ └── challenge7/ # Token Theft
│ └── hard/ # Hard difficulty challenges (8-10)
│ ├── challenge8/ # Malicious Code Execution
│ ├── challenge9/ # Remote Access Control
│ └── challenge10/ # Multi-Vector Attack
├── docs/ # Documentation
│ ├── setup.md # Setup instructions
│ ├── challenges.md # Challenge descriptions
│ └── mcp_overview.md # MCP protocol overview
├── solutions/ # Solution guides
└── common/ # Shared code and utilities
Getting Started
See the Setup Guide for detailed instructions on how to install and run the challenges.
Challenges
The project includes 10 challenges across three difficulty levels:
Easy Challenges
- Basic Prompt Injection: Exploit unsanitized user input to manipulate LLM behavior
- Tool Poisoning: Exploit hidden instructions in tool descriptions
- Excessive Permission Scope: Exploit overly permissive tools to access unauthorized resources
Medium Challenges
- Rug Pull Attack: Exploit tools that change their behavior after installation
- Tool Shadowing: Exploit tool name conflicts to override legitimate tools
- Indirect Prompt Injection: Inject malicious instructions through data sources
- Token Theft: Extract authentication tokens from insecure storage
Hard Challenges
- Malicious Code Execution: Execute arbitrary code through vulnerable tools
- Remote Access Control: Gain remote access to the system through command injection
- Multi-Vector Attack: Chain multiple vulnerabilities for a sophisticated attack
See the Challenges Guide for detailed descriptions of each challenge.
Solutions
Solution guides are provided for educational purposes. It's recommended to attempt the challenges on your own before consulting the solutions.
See the Solutions Guide for detailed solutions to each challenge.
Disclaimer
This project is for educational purposes only. The vulnerabilities demonstrated in this project should never be implemented in production systems. Always follow security best practices when implementing MCP servers.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Author
This project is created by Harish Santhanalakshmi Ganesan using cursor IDE and Manus AI.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.