A Survey on the Unique Security of Autonomous and Collaborative LLM Agents: Threats, Defenses, and Futures

作者：Yinggang Sun, Haining Yu, Wei Jiang, Xiangzhan Yu, Dongyang Zhan, Lixu Wang, Siyue Ren, Yue Sun, Tianqing Zhu · 发表于：Preprints.org · 年份：2026 · DOI：10.20944/preprints202602.1655.v3 · 研究领域：Adversarial Robustness in Machine Learning、Explainable Artificial Intelligence (XAI)、Topic Modeling

The rapid evolution of Large Language Models (LLMs) from static text generators to autonomous agents has revolutionized their ability to perceive, reason, and act within complex environments. However, this transition shifts the security problem from model-level robustness to system-level engineering. The conflation of instructions and data, persistent cognitive states, and untrusted coordination create structural risks that are not adequately captured by traditional adversarial robustness. To address the fragmented nature of the existing literature, this article presents a comprehensive and systematic survey of the security landscape for LLM-based agents. We propose a novel, structure-aware taxonomy that categorizes threats into three distinct paradigms: (1) External Interaction Attacks, which exploit vulnerabilities in perception interfaces and tool usage; (2) Internal Cognitive Attacks, which compromise the integrity of reasoning chains and memory mechanisms; and (3) Multi-Agent Collaboration Attacks, which manipulate communication protocols and collective decision-making. Adapting to this threat landscape, we systematize existing mitigation strategies into a unified defense framework that includes input sanitization, cognitive fortification, and collaborative consensus. In addition, we provide an in-depth comparative analysis of agent-specific security evaluation benchmarks. The survey concludes by outlining critical open problems and future research directions, aiming to ...