Technology Category
Philosophy and Ethics of AI: Privacy & SecurityNatural Language Processing: Safety and RobustnessMachine Learning: Privacy
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
🤖 AI Summary
This paper addresses security and privacy risks arising from the Model Context Protocol (MCP) in AI model–external tool interoperability. We first systematically define MCP’s full lifecycle—encompassing creation, execution, and update phases—and develop a stage-specific threat taxonomy with corresponding mitigation strategies. Integrating protocol design principles, security threat modeling, privacy risk analysis, and industry ecosystem surveys, we propose an MCP security governance guideline, a compatibility mapping across major platforms, and a sustainable development roadmap. Our core contribution is the establishment of the first comprehensive MCP lifecycle security model, unifying technical implementation, platform integration, and ecosystem evolution into a coherent research paradigm. This work provides both theoretical foundations and practical benchmarks for trustworthy AI interoperability. (136 words)
Technology Category
Application Category
📝 Abstract
The Model Context Protocol (MCP) is a standardized interface designed to enable seamless interaction between AI models and external tools and resources, breaking down data silos and facilitating interoperability across diverse systems. This paper provides a comprehensive overview of MCP, focusing on its core components, workflow, and the lifecycle of MCP servers, which consists of three key phases: creation, operation, and update. We analyze the security and privacy risks associated with each phase and propose strategies to mitigate potential threats. The paper also examines the current MCP landscape, including its adoption by industry leaders and various use cases, as well as the tools and platforms supporting its integration. We explore future directions for MCP, highlighting the challenges and opportunities that will influence its adoption and evolution within the broader AI ecosystem. Finally, we offer recommendations for MCP stakeholders to ensure its secure and sustainable development as the AI landscape continues to evolve.
Problem
Research questions and friction points this paper is trying to address.
Standardizing AI model interaction with external tools
Analyzing security risks in MCP lifecycle phases
Exploring future adoption challenges for MCP
Innovation
Methods, ideas, or system contributions that make the work stand out.
Standardized interface for AI model interaction
Three-phase lifecycle for MCP servers
Security strategies for each lifecycle phase
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