Related papers: The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies

The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies

URL: http://arxiv.org/abs/2407.19354v1
Date: Sun, 28 Jul 2024 00:26:24 GMT
Title: The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
Authors: Feng He, Tianqing Zhu, Dayong Ye, Bo Liu, Wanlei Zhou, Philip S. Yu,
Abstract summary: Large Language Models (LLMs) agents have evolved to perform complex tasks. The widespread applications of LLM agents demonstrate their significant commercial value. However, they also expose security and privacy vulnerabilities. This survey aims to provide a comprehensive overview of the newly emerged privacy and security issues faced by LLM agents.
Score: 43.65655064122938
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.