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所在平台: Udemy |
课程主页: https://www.udemy.com/course/securing-multi-agentic-ai-systems/
课程评论：没有评论
课程名称：多智能体人工智能系统的安全 课程概述： 《多智能体人工智能系统的安全》课程深入探讨了基于智能体的人工智能领域及其面临的关键安全挑战。课程首先介绍了多智能体系统（MAS）的结构、自主性和行为模型，随后研究了这些智能体在分布式环境中如何协调、谈判和寻找对等体。接下来，课程探讨了MAS特有的安全隐患，包括信任边界、非确定性行为和身份挑战，并切入由OWASP智能体AI威胁框架定义的应用威胁场景。学习者将调查诸如身份欺骗、工具滥用和内存中毒等具体威胁，并评估它们在现实世界中的MAS失败案例。 课程的核心是MAESTRO框架，这是一种分层的智能体威胁建模方法。参与者学习在模型、内存、编排、工具和基础设施层面映射漏洞，识别新兴行为和跨层利用。专门模块聚焦于模型漂移、提示注入、RAG向量中毒、插件劫持和服务滥用。通过案例研究（如RPA费用智能体），学生参与风险发现、级联故障模拟及自主智能体的红队演练。 课程后期强调检测与防御。学习者设计遥测系统，集成实时威胁情报，并将MAESTRO与MITRE ATT & CK和ATLAS对齐，实现企业级威胁融合。最后，架构模块指导学生通过故障安全设计模式、智能体隔离策略，以及在智能体工作流中实现零信任原则。无论是保护基于大型语言模型的智能体还是区块链集成智能体，本课程都为专业人士提供实践技能和战略模型，以防御下一代自主系统。
The course "Securing Multi-Agentic AI Systems" offers a deep, structured exploration into the evolving field of agent-based artificial intelligence and the critical security challenges it presents. It begins with foundational insights into the structure, autonomy, and behavioral models of Multi-Agent Systems (MAS), followed by an examination of how these agents coordinate, negotiate, and discover peers within distributed environments. The course then delves into the unique security implications of MAS-including trust boundaries, non-deterministic behavior, and identity challenges-before transitioning into applied threat scenarios defined by the OWASP Agentic AI Threat Framework. Learners investigate specific threats such as identity spoofing, tool misuse, and memory poisoning, and assess how these manifest in real-world MAS failures.Central to the course is the MAESTRO framework, a layered approach to agentic threat modeling. Participants learn to map vulnerabilities across model, memory, orchestration, tooling, and infrastructure layers, identifying emergent behavior and cross-layer exploits. Specialized modules focus on model drift, prompt injection, RAG vector poisoning, plugin hijacks, and service abuse. Through case studies-including an RPA Expense Agent -students engage in hands-on risk discovery, simulation of cascading failures, and red-teaming of autonomous agents.The latter part of the course emphasizes detection and defense. Learners design telemetry systems, integrate real-time threat intelligence, and align MAESTRO with MITRE ATT & CK and ATLAS for enterprise-ready threat fusion. Finally, architectural modules guide students through fail-safe design patterns, agent isolation strategies, and the implementation of Zero Trust principles across agent workflows. Whether you're securing LLM-based agents or blockchain-integrated agents, this course equips professionals with practical skills and strategic models to defend the next generation of autonomous systems.
