13 分钟攻击LLM:提示词注入
将提示词注入建模为不可信数据混合问题,并围绕工具调用设计边界。
Fireship. 这是YouTube上对“截图—操作—截图”循环最清晰的短篇讲解,也如实展示了各种故障模式(Claude跑去查看Yellowstone、token消耗、每一步的延迟)。Fireship有意淡化了生产细节——这部分请阅读文章——但它能让你在将此类系统纳入技术栈前,正确理解它们为何成本高昂且十分脆弱。
应将本视频用于建立直觉,而不是作为生产指导。视频有意压缩了主题;生产系统需要沙箱、机密隔离、审计日志、速率限制和回滚路径。
理解与API原生自动化相比,基于截图的计算机操作为何既强大,又缓慢、昂贵且脆弱。
了解LLM调用API工具与LLM控制浏览器或桌面之间的区别。
最后审核:2026年5月18日
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Microsoft Copilot Studio team
The deeper, production-minded counterpart to our beginner no-code pick: a free, open-source, rank-based curriculum that takes you from zero Copilot Studio experience through MCP integrations and multi-agent orchestration, all without writing traditional code. It's the no-code answer to 'now I want to go further than a quick-start,' which our catalog didn't have.
Anthropic Academy
MCP正在悄然取代AI工具生态中为单一用途开发的工具集成。请直接向标准的创建者学习。学完后,你将构建并部署自己的MCP服务器,把一个LLM客户端连接到该服务器,并理解为何这项标准堪称该领域最接近USB-C的方案。
João Moura (Founder, CrewAI)
Doubles as our sales and customer-support vertical pick and a genuinely practical agent-building course: you build an agentic sales pipeline (lead scoring, personalized outreach) and a customer-support data-insights pipeline as two of the five hands-on projects, taught by CrewAI's own founder. Requires basic Python, so it sits with our other builder-track courses rather than the no-code picks.