IMCP - Insecure Model Context Protocol
An educational framework for understanding AI security vulnerabilities
⚠️ Educational Purposes Only
IMCP (Insecure Model Context Protocol) is a deliberately vulnerable application designed exclusively for educational and research purposes. It demonstrates critical AI security vulnerabilities. DO NOT deploy in production environments or use with sensitive data.
🔍 Overview
IMCP is an educational framework that exposes 16 critical security vulnerabilities in AI/ML model serving systems. It serves as a controlled, "vulnerable by design" platform for security researchers, developers, and educators to learn about and mitigate emerging AI threats.
Think of IMCP as the “DVWA for AI” — a safe environment where you can explore:
- Model Poisoning
- Prompt Injection
- Embedding Vector Exploits
- RAG System Weaknesses
- And many more…
🛡️ Vulnerabilities Demonstrated
Core AI Manipulation
- Model Poisoning: Malicious training data injection.
- Token Prediction Attacks: Exploiting token probability for sensitive data extraction.
- Multimodal Vulnerabilities: Cross-modal prompt leakage and metadata manipulation.
- Credential Vulnerabilities: Insecure authentication mechanisms in AI systems.
Information Disclosure
- Embedding Vector Attacks: Poisoning vector stores for unauthorized access.
- RAG Vulnerabilities: Exploiting document stores for cross-user data leakage.
- User Data Leakage: Unintended exposure of conversation histories.
- Model Capability Enumeration: Over-disclosure of internal model details.
Control Manipulation
- Context Manipulation: Unrestricted modifications to model contexts and system prompts.
- Prompt Injection: Techniques to bypass AI safety filters.
- Model Access Control Bypass: Elevation of privileges to access restricted functionalities.
- Model Chain Attacks: Exploiting chained model interactions.
📜 Test Suite
The test suite in test_vulnerabilities.py demonstrates each vulnerability with detailed explanations and examples. It includes:
- Model Poisoning: Injecting malicious data into model responses.
- Token Prediction: Extracting sensitive information character by character.
- Embedding Vector Attacks: Unauthorized access to sensitive embeddings.
- Context Manipulation: Modifying system prompts and configurations.
- Function Calling Vulnerabilities: Registering functions for remote code execution.
- RAG Vulnerabilities: Cross-user document access and manipulation.
📜 API Endpoints
/imcp: Main JSON-RPC endpoint for IMCP functionality./v1/chat/completions: OpenAI API-compatible endpoint./v1/models: List available models./v1/embeddings: Generate embeddings./v1/auth/token: Authentication endpoint./.well-known/imcp-configuration: Service discovery endpoint.
🚀 Getting Started
Prerequisites
- Python 3.8+
- OpenAI API Key (required for live examples)
Installation
Clone the repository and set up your environment:
# Clone the repository
git clone https://github.com/nav33n25/IMCP.git
cd imcp
# Create a virtual environment
python -m venv venv
source venv/bin/activate # For Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Configure the environment
cp .env.example .env
# Edit .env to include your OpenAI API key
Running IMCP
Start the server and run the test suite:
# Start the IMCP server
python -m flask run --host=0.0.0.0 --port=5000
# In another terminal, run the test suite
python test_vulnerabilities.py
📚 Documentation
All the comprehensive guides are located in the documentation/ directory:
- Vulnerability Guide: Detailed explanations of each vulnerability.
- Exploitation Guide: Step-by-step instructions to reproduce each vulnerability.
- Mitigation Guide: Strategies and best practices to secure AI systems.
🌟 Key Features
- Realistic AI Service Implementation
- 16 Unique AI-Specific Security Vulnerabilities
- Comprehensive Test Suite for Demonstrations
- Detailed Documentation for In-Depth Learning
- Compatibility with Modern LLM APIs (e.g., OpenAI)
- Mock Mode for Cost-Free Testing
🤝 Contributing
We welcome contributions from the community! Areas where you can help include:
- Additional Vulnerability Demonstrations: New scenarios or enhancements.
- Improved Documentation: Detailed educational materials and guides.
- Integration: Support for other LLM providers.
- UI Enhancements: Better visualizations and user experience improvements.
Please check out our CONTRIBUTING.md for more details on how to get started.
📜 License
This project is licensed under the MIT License. See the LICENSE file for details.
⚠️ Disclaimer
IMCP is intentionally vulnerable software for educational purposes only. The creators are not liable for any misuse or damage caused by the use of this software.
