面向代理式AI的TRiSM框架:基于大语言模型的代理多智能体系统中信任、风险与安全管理综述
TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems
June 4, 2025
作者: Shaina Raza, Ranjan Sapkota, Manoj Karkee, Christos Emmanouilidis
cs.AI
摘要
基于大型语言模型(LLMs)构建并部署于多智能体配置中的自主AI系统,正在重新定义企业和社会领域中的智能自主性、协作与决策。本综述对基于LLM的自主多智能体系统(AMAS)中的信任、风险与安全管理(TRiSM)进行了结构化分析。首先,我们探讨了自主AI的概念基础,其与传统AI智能体在架构上的差异,以及支持可扩展、工具使用自主性的新兴系统设计。随后,通过治理、可解释性、模型运维(ModelOps)及隐私/安全四大支柱,详细阐述了自主AI框架中的TRiSM,每一支柱均针对自主LLMs进行了情境化分析。我们识别了独特的威胁向量,并引入了一套全面的风险分类体系,辅以展示现实世界漏洞的案例研究。此外,本文还调查了分布式LLM智能体系统中的信任构建机制、透明度与监督技术,以及最先进的可解释性策略。同时,回顾了评估信任、可解释性及以人为中心性能的指标,并指出了开放基准测试的挑战。通过加密、对抗防御及遵守不断演进的AI法规,解决了安全与隐私问题。文章最后提出了负责任自主AI的发展路线图,建议研究新兴多智能体系统如何与稳健的TRiSM原则对齐,以实现安全、可问责且透明的部署。
English
Agentic AI systems, built on large language models (LLMs) and deployed in
multi-agent configurations, are redefining intelligent autonomy, collaboration
and decision-making across enterprise and societal domains. This review
presents a structured analysis of Trust, Risk, and Security Management (TRiSM)
in the context of LLM-based agentic multi-agent systems (AMAS). We begin by
examining the conceptual foundations of agentic AI, its architectural
differences from traditional AI agents, and the emerging system designs that
enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is
then detailed through four pillars governance, explainability, ModelOps, and
privacy/security each contextualized for agentic LLMs. We identify unique
threat vectors and introduce a comprehensive risk taxonomy for the agentic AI
applications, supported by case studies illustrating real-world
vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms,
transparency and oversight techniques, and state-of-the-art explainability
strategies in distributed LLM agent systems. Additionally, metrics for
evaluating trust, interpretability, and human-centered performance are reviewed
alongside open benchmarking challenges. Security and privacy are addressed
through encryption, adversarial defense, and compliance with evolving AI
regulations. The paper concludes with a roadmap for responsible agentic AI,
proposing research directions to align emerging multi-agent systems with robust
TRiSM principles for safe, accountable, and transparent deployment.Summary
AI-Generated Summary