SMCP: Secure Model Context Protocol

2026-02-01

AI Summary

This work addresses critical security and privacy risks in the Model Context Protocol (MCP) within open agent ecosystems, including missing authentication, tool poisoning, prompt injection, privilege escalation, and supply chain attacks. To mitigate these threats, the paper proposes the first systematic security enhancement framework tailored for MCP, which uniformly implements identity management, mutual strong authentication, continuous secure context propagation, fine-grained access control, and comprehensive audit logging at the protocol layer. This framework fills a significant gap in existing research by establishing a robust foundation for trustworthy communication and collaboration among open intelligent agents, effectively countering a wide range of known vulnerabilities while preserving the protocol’s functional integrity.

Technology Category

• Multiagent Systems: Agent Communication
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that connect a variety of agents, external tools, and resources. The Model Context Protocol (MCP) has emerged as a standard to unify tool access, allowing agents to discover, invoke, and coordinate with tools more flexibly. However, as MCP becomes more widely adopted, it also brings a new set of security and privacy challenges. These include risks such as unauthorized access, tool poisoning, prompt injection, privilege escalation, and supply chain attacks, any of which can impact different parts of the protocol workflow. While recent research has examined possible attack surfaces and suggested targeted countermeasures, there is still a lack of systematic, protocol-level security improvements for MCP. To address this, we introduce the Secure Model Context Protocol (SMCP), which builds on MCP by adding unified identity management, robust mutual authentication, ongoing security context propagation, fine-grained policy enforcement, and comprehensive audit logging. In this paper, we present the main components of SMCP, explain how it helps reduce security risks, and illustrate its application with practical examples. We hope that this work will contribute to the development of agentic systems that are not only powerful and adaptable, but also secure and dependable.

Problem

• Model Context Protocol
• security
• privacy
• agentic AI
• LLM

Innovation

• Secure Model Context Protocol
• mutual authentication
• security context propagation
• fine-grained policy enforcement
• audit logging