首份系统综述揭示智能体AI的攻防全景与安全框架
The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey
- 构建首个智能体AI安全风险与防御策略的系统框架
- 分析智能体系统设计空间、攻击面及现有防御机制
- 指出当前安全短板，为研究者和开发者提供方向
结合大语言模型与非AI组件的AI智能体正快速应用于现实场景，带来前所未有的自动化与灵活性。然而，这种灵活性也引入了与传统软件系统截然不同的复杂安全挑战。本文首次系统性地综述了智能体AI的安全问题，涵盖其设计空间、攻击图谱与防御机制。通过案例研究，揭示了当前智能体系统在安全性方面的现存缺口，并指出了该新兴领域中的开放性挑战。本工作提出了首个系统化框架，用于理解智能体AI的安全风险与防御策略，为构建安全智能体系统及推动该关键领域的研究奠定基础。
原文摘要 · Abstract (English)
AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in traditional software systems. This paper presents the first systematic and comprehensive survey of AI agent security, including an analysis of the design space, attack landscape, and defense mechanisms for secure AI agent systems. We further conduct case studies to point out existing gaps in securing agentic AI systems and identify open challenges in this emerging domain. Our work also introduces the first systematic framework for understanding the security risks and defense strategies of AI agents, serving as a foundation for building both secure agentic systems and advancing research in this critical area.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。