harishsg993010/damn-vulnerable-MCP-server
Damn Vulnerable MCP Server
This project is an educational and research platform designed to simulate security vulnerabilities within AI-integrated systems and Model Context Protocol implementations. It provides a controlled environment where users can practice identifying and mitigating common attack vectors, such as prompt injection and unauthorized code execution, by interacting with intentionally insecure tools and protocol configurations.
The platform distinguishes itself by offering a dedicated laboratory for auditing Model Context Protocol integrations. It exposes server-side functions as discoverable tools and provides structured access to sensitive data, allowing researchers to test the resilience of AI applications against malicious instructions and unrestricted resource access.
The framework covers a broad range of security testing capabilities, including the simulation of insecure sandbox environments and the analysis of request-response messaging protocols. These resources support the evaluation of defense strategies and the study of how language models interact with external tools in potentially compromised settings.
Features
- Model Context Protocol Security - Provides a dedicated laboratory for auditing the security posture of Model Context Protocol implementations and tool interactions.
- AI Application Security - Serves as an educational platform for practicing exploit techniques and mitigation strategies within AI-integrated software environments.
- Prompt Context Injections - Provides mechanisms for injecting malicious instructions into dialogue strings to test system resilience against prompt manipulation.
- Vulnerable Environments - Provides intentionally insecure tools and resources to practice exploit techniques within AI-enabled application environments.
- Server Capability Exposure - Exposes server-side functions as discoverable tools, enabling language models to interact with and invoke specific capabilities.
- Sandboxed Execution Environments - Ships isolated execution environments designed to safely run untrusted code snippets for security research and vulnerability testing.
- Web Security Training Environments - Offers a sandboxed environment for testing exploit techniques against AI-enabled interfaces to demonstrate common attack vectors.
- JSON-RPC Implementations - Implements the JSON-RPC protocol to facilitate standardized, stream-based communication between AI clients and external tool servers.
- AI Security Research - Facilitates research into security flaws by providing intentionally vulnerable tools and protocol implementations for testing.
- Prompt Injection Defenses - Evaluates large language model resilience against prompt injection by simulating insecure tool execution environments.
- Vulnerability Research - Acts as a framework for simulating and analyzing security weaknesses in integrations between language models and external tools.
- Resource Access Simulations - Provides structured access to sensitive data resources to evaluate the effectiveness of authorization and access control mechanisms.
- Simulated Vulnerabilities - Simulates common attack vectors like prompt injection and unauthorized code execution to identify potential system weaknesses.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
