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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-deep-dive-into-llm-red-teaming/
课程评论：没有评论
## Coursera 课程总结：深入 LLM 红队演练 **课程名称：** LLM 红队演练：攻防大型语言模型 **课程概述：** 本课程是为人工智能从业者、网络安全爱好者和红队成员打造的终极实践课程，旨在深入探讨人工智能的最新漏洞。课程将带领您深入 LLM 安全领域，教授您如何运用真实世界的技巧来攻击和防御大型语言模型。您将学习提示注入、越狱、间接提示攻击和系统消息操纵等内容。 无论您是旨在对 AI 系统进行压力测试的红队成员，还是正在构建更安全 LLM 应用的开发者，本课程都将为您提供像对手一样思考、像专家一样防御的工具。 课程将逐步演示直接和间接注入场景，展示如何构建基于提示的漏洞利用，并探索多轮操纵和在看似无害的用户输入中植入恶意意图等高级策略。您还将学习如何设计自己的测试框架，并利用开源工具自动化漏洞发现。 **学习目标：** * 掌握人工智能的对抗性测试方法。 * 理解大型语言模型（LLM）可能遭受的攻击方式。 * 学习如何构建更强大的 AI 系统。 * 熟悉各种 LLM 攻击技术，包括提示注入、越狱、间接提示攻击等。 * 学会设计和使用测试框架，自动发现 LLM 漏洞。 **目标受众：** * 人工智能从业者 * 网络安全爱好者 * 红队成员 * 希望深入了解 LLM 安全的开发者 **总结：** 如果您希望在 AI 的进攻和防御两方面都取得精通，这门课程绝对是您的不二之选。它提供了宝贵的实践知识和工具，使您能够有效地评估和加固大型语言模型的安全性。
Welcome to LLM Red Teaming: Hacking and Securing Large Language Models - the ultimate hands-on course for AI practitioners, cybersecurity enthusiasts, and red teamers looking to explore the cutting edge of AI vulnerabilities.This course takes you deep into the world of LLM security by teaching you how to attack and defend large language models using real-world techniques. You'll learn the ins and outs of prompt injection, jailbreaks, indirect prompt attacks, and system message manipulation. Whether you're a red teamer aiming to stress-test AI systems, or a developer building safer LLM applications, this course gives you the tools to think like an adversary and defend like a pro.We'll walk through direct and indirect injection scenarios, demonstrate how prompt-based exploits are crafted, and explore advanced tactics like multi-turn manipulation and embedding malicious intent in seemingly harmless user inputs. You'll also learn how to design your own testing frameworks and use open-source tools to automate vulnerability discovery.By the end of this course, you'll have a strong foundation in adversarial testing, an understanding of how LLMs can be exploited, and the ability to build more robust AI systems.If you're serious about mastering the offensive and defensive side of AI, this is the course for you.
