  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.
