Damn Vulnerable Model Context Protocol (DVMCP)

# Model Context Protocol (MCP) Overview This document provides an overview of the Model Context Protocol (MCP), its architecture, and security considerations. ## What is MCP? The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 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. MCP was developed to address the challenges of integrating LLMs with external data sources and tools. It provides a standardized way for applications to expose their functionality to LLMs, allowing for more powerful and flexible AI applications. ## Core Concepts ### Server An MCP server is an application that implements the Model Context Protocol and exposes resources, tools, and prompts to LLM clients. Servers can be standalone applications or integrated into existing applications. ### Resources Resources are data sources that can be accessed by LLMs. They provide information that the LLM can use to generate responses. Resources are identified by URIs and can be static or dynamic. Examples of resources: - `company://public` - Public information about a company - `files://{filename}` - Content of a specific file - `database://customers/{id}` - Information about a specific customer ### Tools Tools are functions that can be called by LLMs to perform actions or retrieve information. They allow LLMs to interact with the outside world and perform tasks that would otherwise be impossible. Examples of tools: - `search_database(query)` - Search a database for information - `send_email(to, subject, body)` - Send an email - `calculate(expression)` - Perform a calculation ### Prompts Prompts are templates for LLM interactions. They provide a structured way to define how LLMs should respond to specific types of requests. ### Context Context is the information provided to an LLM during a conversation. It includes resources, tools, and other data that the LLM can use to generate responses. ## Protocol Architecture MCP uses a client-server architecture: 1. **MCP Server**: Implements the protocol and exposes resources and tools 2. **MCP Client**: Connects to the server and provides the context to the LLM 3. **LLM**: Processes the context and generates responses The communication flow is as follows: 1. The client connects to the server 2. The server provides information about available resources and tools 3. The client requests specific resources or calls tools 4. The server responds with the requested information or tool results 5. The client incorporates this information into the LLM's context 6. The LLM generates a response based on the context ## Security Considerations While MCP provides many benefits, it also introduces new security considerations: ### Prompt Injection Prompt injection occurs when an attacker manipulates the input to an LLM to make it ignore its previous instructions or perform unintended actions. In MCP, this can happen through user input that is passed to resources or tools without proper validation. ### Tool Poisoning Tool poisoning involves hiding malicious instructions in tool descriptions or metadata. Since LLMs process the entire context, including tool descriptions, these hidden instructions can manipulate the LLM's behavior. ### Excessive Permissions If MCP tools are granted more permissions than necessary, they can be exploited to access unauthorized resources or perform unauthorized actions. ### Rug Pull Attacks A rug pull attack occurs when a tool changes its behavior after it has been installed and approved. This can happen if the tool's implementation or description is modified dynamically. ### Tool Shadowing When multiple MCP servers are connected, a malicious server can define tools with the same names as those from a trusted server, effectively "shadowing" the legitimate tools. ### Indirect Prompt Injection Indirect prompt injection involves embedding malicious instructions in data sources that are later processed by the LLM. This can happen if the MCP server retrieves and processes external data without proper validation. ### Token Theft If authentication tokens for external services are stored insecurely, they can be extracted and used to gain unauthorized access to those services. ### Malicious Code Execution If MCP tools execute code without proper validation or sandboxing, attackers can run arbitrary code on the system. ### Remote Access Control If MCP tools pass unvalidated input to system commands, attackers can inject malicious commands to gain remote access to the system. ## Best Practices To mitigate these security risks, MCP implementations should follow these best practices: 1. **Validate and sanitize all user input** before passing it to resources or tools 2. **Limit tool permissions** to the minimum necessary for their intended function 3. **Sandbox code execution** to prevent arbitrary code execution 4. **Validate tool descriptions** to prevent hidden instructions 5. **Implement proper authentication and authorization** for accessing sensitive resources 6. **Securely store authentication tokens** for external services 7. **Monitor and log all MCP interactions** to detect potential attacks 8. **Regularly audit MCP servers** for security vulnerabilities 9. **Keep MCP implementations up to date** with the latest security patches 10. **Educate developers** about MCP security risks and best practices ## Further Reading - [Model Context Protocol Specification](https://modelcontextprotocol.io/specification/2025-03-26/index) - [MCP Python SDK Documentation](https://github.com/modelcontextprotocol/python-sdk) - [MCP Security Best Practices](https://modelcontextprotocol.io/security)