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Secure Your AI Agents – Free Model Context Protocol (MCP) Security Course

8.5内容质量

TL;DR · AI 摘要

AI代理通过MCP连接外部系统时存在重大安全风险，需通过架构设计而非提示过滤器防御。

核心要点

  • AI代理同时具备私有数据、外部内容和通信能力时，会形成结构性安全漏洞。
  • 防御应聚焦架构边界而非依赖提示过滤器，课程提供具体实现方法。
  • 免费课程2小时覆盖MCP安全核心，含Simon Willison的'Lethal Trifecta'框架解析。

结构提纲

按章节快速跳转。

  1. §引言

    MCP协议连接LLM与外部系统时引入新型攻击面，需专门安全课程应对。

  2. ·传统安全模型局限

    传统应用安全假设确定性代码，但AI代理引入非确定性决策机制。

  3. ·Lethal Trifecta框架

    私有数据、外部内容和通信能力三者共存时形成结构性漏洞。

  4. ›防御策略

    通过架构边界设计打破三要素共存，而非依赖脆弱的提示过滤器。

  5. ·课程内容

    2小时课程解析攻击机制并提供防御方案，含实战案例演示。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • MCP安全课程
    • 核心机制
      • 非确定性决策风险
      • 攻击面扩展
    • Lethal Trifecta
      • 私有数据
      • 外部内容
      • 通信能力
    • 防御策略
      • 架构边界设计
      • 三要素隔离

金句 / Highlights

值得收藏与分享的关键句。

#AI安全#MCP#AI代理#网络安全
打开原文

Secure Your AI Agents – Free Model Context Protocol (MCP) Security Course

October 29, 2026

/

#mcp

Beau Carnes

The Model Context Protocol (MCP) helps connect LLMs to external tools, databases, and APIs. But giving AI agents hands and feet to take real-world actions introduces an entirely new attack surface.

We just published a MCP Security Fundamentals Course on the freeCodeCamp.org YouTube channel.

This course walks you through the mechanics of agent exploitation and how to defend against them.

Traditional application security assumes deterministic code and predictable human or machine interactions. AI agents, however, introduce non-deterministic decision-makers right into your application logic. When an agent acts on untrusted data, classic bugs like command injection, privilege escalation, and token leakage execute at machine speed.

Throughout the course, Dan frames these challenges around Simon Willison’s "Lethal Trifecta" framework, which is the dangerous convergence of three distinct capabilities:

  • Private Data The agent has read access to sensitive internal records, source code, credentials, or private databases.
  • Untrusted Content The agent ingests text from outside sources, including web pages, emails, customer tickets, public issues, or third-party tool schemas.
  • External Communications The agent possesses the capability to transmit data outward through webhooks, markdown rendering, emails, or API calls.

When an agent holds all three capabilities simultaneously, it becomes structurally vulnerable to prompt-driven exploitation. You will learn how to break this trifecta through architectural boundaries rather than relying on brittle prompt filters.

Watch the full course for free on the freeCodeCamp.org YouTube channel and start hardening your AI agent integrations today (2-hour watch).

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I'm a teacher and developer with freeCodeCamp.org. I run the freeCodeCamp.org YouTube channel.

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