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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-ai-llm-red-teaming/
课程评论：没有评论
课程名称：实战AI（LLM）红队训练 课程概述： 本课程旨在提供有关AI安全的实战培训，重点关注大型语言模型（LLM）的红队技术，专为进攻性网络安全研究人员、AI从业者和网络安全团队经理设计。培训旨在使参与者掌握以下技能： - 发现并利用AI系统中的漏洞（出于伦理目的）。 - 保护AI系统免受攻击。 - 在组织内实施AI治理和安全措施。 学习目标： - 理解生成式AI的风险和漏洞。 - 探索如欧盟AI法案等监管框架和新兴AI安全标准。 - 获得测试和保护LLM系统的实用技能。 课程结构： 1. AI红队简介： - LLM的架构 - LLM风险分类 - 红队策略和工具概述 2. 破解LLMs： - LLM监狱式破解技术 - 漏洞测试的实战练习 3. 提示注入： - 提示注入基础及其与监狱式破解的区别 - 进行和防止提示注入的技术 - RAG（检索增强生成）和代理架构的实用练习 4. LLM的OWASP十大风险： - 理解常见风险 - 演示以强化概念 - 引导红队练习以测试和减轻这些风险 5. 实施工具和资源： - 红队的Jupyter笔记本、模板和工具 - 实施保护措施和监控解决方案的安全工具分类 课程成果： - 知识增强：提升对AI安全术语、框架和战术的理解。 - 实用技能：获得红队LLM及风险缓解的实战经验。 - 框架开发：为组织构建AI治理和安全成熟度模型。 适合参加的人群： 本课程理想适合于： - 进攻性网络安全研究人员 - 聚焦于防御和安全的AI从业者 - 希望建立和引导AI安全团队的经理 祝好运，期待在课程中见到你！
ObjectiveThis course provides hands-on training in AI security, focusing on red teaming for large language models (LLMs). It is designed for offensive cybersecurity researchers, AI practitioners, and managers of cybersecurity teams. The training aims to equip participants with skills to:Identify and exploit vulnerabilities in AI systems for ethical purposes.Defend AI systems from attacks.Implement AI governance and safety measures within organizations.Learning GoalsUnderstand generative AI risks and vulnerabilities.Explore regulatory frameworks like the EU AI Act and emerging AI safety standards.Gain practical skills in testing and securing LLM systems.Course StructureIntroduction to AI Red Teaming:Architecture of LLMs.Taxonomy of LLM risks.Overview of red teaming strategies and tools.Breaking LLMs:Techniques for jailbreaking LLMs.Hands-on exercises for vulnerability testing.Prompt Injections:Basics of prompt injections and their differences from jailbreaking.Techniques for conducting and preventing prompt injections.Practical exercises with RAG (Retrieval-Augmented Generation) and agent architectures.OWASP Top 10 Risks for LLMs:Understanding common risks.Demos to reinforce concepts.Guided red teaming exercises for testing and mitigating these risks.Implementation Tools and Resources:Jupyter notebooks, templates, and tools for red teaming.Taxonomy of security tools to implement guardrails and monitoring solutions.Key OutcomesEnhanced Knowledge: Develop expertise in AI security terminology, frameworks, and tactics.Practical Skills: Hands-on experience in red teaming LLMs and mitigating risks.Framework Development: Build AI governance and security maturity models for your organization.Who Should Attend?This course is ideal for:Offensive cybersecurity researchers.AI practitioners focused on defense and safety.Managers seeking to build and guide AI security teams.Good luck and see you in the sessions!
