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A comprehensive security checklist for MCP-based AI tools. Built by SlowMist to safeguard LLM plugin ecosystems.
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| 2026-08-05 | 835 |
| 2026-08-06 | 835 |
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# MCP Security Checklist: A Security Guide for the AI Tool Ecosystem [](https://twitter.com/slowmist_team) [English Version](./README.md), [中文版本](./README_CN.md) ## Author Information This security checklist was compiled and is maintained by [@SlowMist_Team](https://twitter.com/slowmist_team). > SlowMist, a global leader in blockchain ecosystem threat intelligence, aims to enhance security protection and safeguard user privacy during the integration of blockchain and AI ecosystems. We sincerely thank [FENZ.AI](https://fenz.ai/) for their valuable contributions and support. > FENZ.AI redefines AI security with future-proof auditing. FENZ is the essential infrastructure for the AI era: "Superintelligence starts with super safety." ## 📚 Table of Contents - [Overview](#overview) - [How to Use](#how-to-use) - [MCP Server (MCP Plugin) Security](#mcp-servermcp-plugin-security) - [MCP Client/MCP HOST Security](#mcp-clientmcp-host-security) - [MCP Adaptation and Invocation Security on Different LLMs](#mcp-adaptation-and-invocation-security-on-different-llms) - [Multi-MCP Scenario Security](#multi-mcp-scenario-security) - [Unique Security Points for Cryptocurrency-related MCPs](#unique-security-points-for-cryptocurrency-related-mcps) - [MCP Security Self-Assessment Tools](#MCP-Security-Self-Assessment-Tools) - [References](#references) ## Overview With the rapid development of large language models (LLMs), a variety of new AI tools have continued to emerge. Among them, tools based on the Model Context Protocol (MCP) standard have become a key bridge connecting LLMs with external tools and data sources. Since its release in late 2024, MCP has been widely adopted in mainstream AI applications such as Claude Desktop and Cursor. Various MCP Server marketplaces have also emerged, demonstrating strong ecosystem scalability. However, the rapid adoption of MCP has also introduced new security challenges. In the current MCP architecture, the system consists of three main components: the **Host** (the local environment where the AI application runs), the **Client** (responsible for communicating with the Server and invoking tools), and the **Server** (the backend service corresponding to an MCP plugin). Users interact with the AI through the Host, while the Client parses the user's request and forwards it to the MCP Server for tool invocation or resource access. In scenarios involving multiple instances and cross-component collaboration, this architecture exposes a range of security risks—especially in sensitive contexts such as cryptocurrency transactions or custom plugin integration with LLMs—where the potential for exploitation is even higher and requires appropriate security controls. Against this backdrop, **establishing and following a comprehensive MCP Security Checklist becomes critically important**. This checklist covers key areas ranging from user interface interaction, client components, and service-side plugins, to multi-MCP collaboration mechanisms and domain-specific scenarios such as cryptocurrency integrations. It is designed to help developers systematically identify and mitigate potential risks. By implementing these security measures, the overall stability and controllability of MCP systems can be significantly enhanced, ensuring that security evolves in parallel with the rapid advancement of AI applications. <p align="center"> <img src="assets/mcp_risk_points_en.png" alt="MCP flow risk diagram" /> </p> <p align="center" style="font-style: italic; color: gray;"> Figure: Schematic diagram of MCP multi-component interaction process and key risk points </p> ## How to Use This checklist is based on possible risk points encountered during our security audits of MCP projects, aiming to help developers ensure the security of MCP implementations. We use three priority levels to denote th
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matched fp:a4f834b235cc38df, llm:description: 'A comprehensive security checklist for MCP-based AI tools. Built by SlowMist to safeguard LLM plugin ecosystems.'
matched fp:a4f834b235cc38df, llm:description: 'A comprehensive security checklist for MCP-based AI tools. Built by SlowMist to safeguard LLM plugin ecosystems.'
matched fp:a4f834b235cc38df, llm:description: 'A comprehensive security checklist for MCP-based AI tools. Built by SlowMist to safeguard LLM plugin ecosystems.'