Inspired by the rapid development of Large Language Models (LLMs), LLM agents have evolved to perform complex tasks. LLM agents are now extensively applied across various domains, handling vast amounts of data to interact with humans and execute tasks. The widespread applications of LLM agents demonstrate their significant commercial value; however, they also expose security and privacy vulnerabilities. At the current stage, comprehensive research on the security and privacy of LLM agents is highly needed. This survey aims to provide a comprehensive overview of the newly emerged privacy and security issues faced by LLM agents. We begin by introducing the fundamental knowledge of LLM agents, followed by a categorization and analysis of the threats. We then discuss the impacts of these threats on humans, environment, and other agents. Subsequently, we review existing defensive strategies, and finally explore future trends. Additionally, the survey incorporates diverse case studies to facilitate a more accessible understanding. By highlighting these critical security and privacy issues, the survey seeks to stimulate future research towards enhancing the security and privacy of LLM agents, thereby increasing their reliability and trustworthiness in future applications.

翻译：受大型语言模型（LLMs）快速发展的启发，LLM智能体已演化为能够执行复杂任务的系统。目前，LLM智能体广泛应用于各个领域，处理海量数据以与人类交互并执行任务。LLM智能体的广泛应用展现了其巨大的商业价值，然而也暴露了其安全与隐私方面的脆弱性。现阶段，亟需对LLM智能体的安全与隐私进行全面研究。本综述旨在全面概述LLM智能体所面临的新兴隐私与安全问题。我们首先介绍LLM智能体的基础知识，随后对威胁进行分类与分析。接着，我们讨论这些威胁对人类、环境及其他智能体的影响。之后，我们回顾现有的防御策略，并最终探讨未来趋势。此外，本综述结合了多样化的案例研究，以促进更易于理解的学习。通过强调这些关键的安全与隐私问题，本综述旨在激发未来的研究，以增强LLM智能体的安全性与隐私性，从而提升其在未来应用中的可靠性与可信度。