Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions (2503.23278v2)

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.

• The paper presents the first comprehensive analysis of MCP, unifying fragmented AI-tool interactions into a dynamic, standardized protocol.
• It outlines MCP’s three-tier architecture—host, client, and server—and its secure, bidirectional Transport Layer for real-time data exchange.
• The research identifies key security threats across the MCP lifecycle and recommends measures like cryptographic verification and centralized registries.

This paper provides the first comprehensive analysis of the Model Context Protocol (MCP), a standardized interface designed to facilitate seamless interaction between AI models and external tools or resources. MCP aims to overcome the fragmentation seen in previous approaches like manual API wiring, plugin interfaces (e.g., ChatGPT plugins), AI agent frameworks (e.g., LangChain), and Retrieval-Augmented Generation (RAG) by offering a unified, dynamic, and platform-agnostic protocol.

MCP Architecture and Workflow

The MCP architecture consists of three main components:

1. MCP Host: An AI application (like Claude Desktop, Cursor IDE, or an autonomous AI agent) that provides the execution environment and runs the MCP client.
2. MCP Client: An intermediary within the host that manages communication between the host and MCP servers. It discovers server capabilities, sends requests, processes responses and notifications, and handles data sampling.
3. MCP Server: Enables access to external systems and offers three core capabilities:
   • Tools: Allows the server to invoke external APIs/services to perform operations (e.g., accessing real-time data, executing code). Unlike function calling, MCP tools allow models to autonomously select and invoke based on context using a standardized supply-and-consume model.
   • Resources: Provides access to structured/unstructured data (local files, databases, APIs) for AI models.
   • Prompts: Offers reusable templates and workflows to streamline repetitive tasks and ensure response consistency.

Communication occurs over a Transport Layer, enabling secure, bidirectional, real-time data exchange. The typical workflow involves the client querying a server for its capabilities, receiving a response detailing available tools/resources/prompts, and then exchanging requests and notifications to execute tasks.

MCP Server Lifecycle and Components

The paper defines a three-phase lifecycle for MCP servers:

1. Creation: Involves server registration (assigning a unique name), installer deployment (setting up code, configuration, manifest), and code integrity verification (preventing tampering).
2. Operation: The active phase where the server handles requests, executes tools, processes slash commands (user-invoked shortcuts), and enforces sandbox mechanisms for secure execution isolation.
3. Update: Focuses on maintaining the server through authorization management (validating permissions post-update), version control (ensuring consistency), and old version management (deactivating vulnerable versions).

Key MCP server components include metadata, configuration files, source code, a tool list, a resources list, and prompts/templates.

Current Landscape and Adoption

MCP is seeing rapid adoption:

• Key Adopters: Anthropic (Claude Desktop), OpenAI (Agent SDK, planned API/ChatGPT integration), Baidu Maps, developer tools (Replit, Microsoft Copilot Studio, Sourcegraph Cody, Codeium, Cursor), IDEs/Editors (Zed, JetBrains, TheiaIDE), cloud platforms (Cloudflare for remote hosting), and financial services (Block, Stripe).
• Community Ecosystem: Numerous community-driven server collections have emerged (e.g., MCP.so, Glama, PulseMCP, Smithery) along with desktop management tools (Dockmaster, Toolbase) due to the lack of an official marketplace.
• SDKs and Tools: Official SDKs exist for TypeScript, Python, Java, etc. Community tools simplify development (EasyMCP, FastMCP), enable auto-generation (FastAPI to MCP), provide libraries (Foxy Contexts), extend gateways (Higress), and offer management platforms (Mintlify, Speakeasy).

Use Cases Highlighted:

• OpenAI: Using MCP in its Agent SDK for standardized tool interaction, with plans for broader integration.
• Cursor: Leveraging MCP in its AI-powered IDE to allow code assistants to interact with external APIs, repos, and automate development tasks.
• Cloudflare: Enabling remote MCP server hosting, offering scalability, OAuth integration, and multi-tenancy, shifting MCP from purely local deployments.

Security and Privacy Analysis

The paper identifies significant security risks across the MCP server lifecycle:

• Creation Phase Risks:
  • Name Collision: Malicious servers impersonating legitimate ones due to similar names, potentially leading to data exposure or unauthorized actions. Aggravated in multi-tenant settings and potential future marketplaces (supply chain risk).
  • Installer Spoofing: Unofficial auto-installers (like Smithery CLI, mcp-get) could distribute compromised packages or misconfigure servers, bypassing user scrutiny.
  • Code Injection/Backdoor: Malicious code embedded in server source, configuration, or dependencies, potentially persisting across updates.
• Operation Phase Risks:
  • Tool Name Conflicts: Multiple tools with similar names causing ambiguity and incorrect tool invocation. Attackers can also use deceptive descriptions to manipulate tool selection ("toolflow hijacking").
  • Slash Command Overlap: Identical commands from different tools leading to unintended actions or hijacking, similar to risks seen in platforms like Slack.
  • Sandbox Escape: Exploiting vulnerabilities in the tool execution sandbox to gain unauthorized access to the host system.
• Update Phase Risks:
  • Post-Update Privilege Persistence: Revoked permissions (e.g., API keys, roles) remaining active after an update, allowing continued unauthorized access.
  • Re-deployment of Vulnerable Versions: Users installing or rolling back to outdated, insecure versions due to the decentralized nature, lack of audits, reliance on unofficial installers, or delayed community patches.
  • Configuration Drift: Unintended configuration changes accumulating over time, deviating from the security baseline, especially risky in remote/multi-tenant environments like Cloudflare's.

Discussion, Challenges, and Recommendations

• Implications: MCP simplifies development but requires vigilance regarding security. It enhances user experience but introduces risks from unverified tools. Maintainers face challenges with decentralized security and configuration consistency. The AI community sees potential for advanced agentic workflows but must address ethical and operational concerns.
• Challenges: Lack of centralized security oversight and package management; gaps in authentication/authorization standards; insufficient debugging/monitoring tools; difficulty maintaining consistency in complex, multi-step workflows; scalability and isolation issues in multi-tenant environments; complexities in embedding MCP securely in smart environments.
• Recommendations:
  • For Maintainers: Establish formal package management, a centralized server registry, cryptographic verification, security audits, and robust sandboxing.
  • For Developers: Use secure coding practices, manage versions diligently, automate configuration (IaC), validate tool names, implement monitoring.
  • For Researchers: Conduct security analyses (sandbox, privileges, configuration), develop better version control/package management approaches for decentralized systems, explore context-aware orchestration and state management.
  • For End-Users: Use verified servers, avoid unofficial installers, update regularly, monitor configurations, manage access controls carefully.

The paper concludes that while MCP holds significant promise for standardizing AI-tool interactions and enabling more powerful AI agents, addressing the identified security, privacy, and governance challenges is crucial for its sustainable growth and trustworthy adoption.