A Survey on Trustworthy LLM Agents: Threats and Countermeasures
Обзор заслуживающих доверия агентов на основе больших языковых моделей: угрозы и контрмеры
2025-08-03
SCID: 54.1/knvs5kyv
Discuss with AI
LLM agent threatsTrustAgent frameworkmulti-agent systemstrustworthiness taxonomytrustworthy LLM agents
Figures from the paper
Abstract (AI)
With the rapid evolution of Large Language Models (LLMs), LLMbased agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems.This evolution stems from empowering LLMs with additional modules such as memory, tools, environment, and even other agents.However, this advancement has also introduced more complex issues of trustworthiness, which previous research focusing solely on LLMs could not cover.In this survey, we propose the TrustAgent framework, a comprehensive study on the trustworthiness of agents, characterized by modular taxonomy, multi-dimensional connotations, and * Miao Yu and Fanci Meng contribute equally to this paper.
Key Findings
1
Adding these modules introduces complex trustworthiness challenges that are not covered by research focused solely on standalone LLMs.
2
LLM agents and multi-agent systems expand LLM capabilities by integrating memory, tools, environments, and other agents.
3
The survey proposes the TrustAgent framework to comprehensively study trustworthy LLM agents.
4
TrustAgent characterizes agent trustworthiness through a modular taxonomy and multi-dimensional connotations.
Research Object
LLM-based agents and multi-agent systems (MAS)
Research Subject
Their trustworthiness, including associated threats and countermeasures
Publication Details
Publication Date
2025-08-03
Journal
Publisher
ISSN
Cited by
27
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest