大模型智能体协同已成现实，本文系统梳理其技术与安全挑战。
Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
- 构建基于大模型的通用智能体框架，支持自主协作
- 提出从数据、计算、知识三维度的协同机制
- 揭示多智能体场景下的安全风险，指引未来研究方向
随着大模型（LMs）的快速发展，由大模型驱动的通用智能体已具备实际应用能力。未来，大模型驱动的通用人工智能代理将在生产任务中作为核心工具，实现无需人工干预的自主通信与协作。本文探讨未来大模型智能体的自主协同场景，综述当前大模型智能体的发展现状、促成协同的关键技术，以及协同过程中面临的安全与隐私挑战。首先，分析大模型智能体的基础原理，包括通用架构、关键组件、使能技术及现代应用场景。其次，从数据、计算和知识三个视角，讨论实现智能体间连接智能的实际协作范式。随后，剖析大模型智能体在多智能体环境中的安全漏洞与隐私风险，探究其内在机理并回顾现有及潜在的防御措施。最后，提出构建稳健且安全的大模型智能体生态系统的未来研究方向。
原文摘要 · Abstract (English)
With the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality. It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks, capable of autonomous communication and collaboration without human intervention. This paper investigates scenarios involving the autonomous collaboration of future LM agents. We review the current state of LM agents, the key technologies enabling LM agent collaboration, and the security and privacy challenges they face during cooperative operations. To this end, we first explore the foundational principles of LM agents, including their general architecture, key components, enabling technologies, and modern applications. We then discuss practical collaboration paradigms from data, computation, and knowledge perspectives to achieve connected intelligence among LM agents. After that, we analyze the security vulnerabilities and privacy risks associated with LM agents, particularly in multi-agent settings, examining underlying mechanisms and reviewing current and potential countermeasures. Lastly, we propose future research directions for building robust and secure LM agent ecosystems.
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