The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
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.
Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 84 of its 143 references that are in the map. First posted on arXiv 28 Jul 2024.
Builds on
84 of 143 references.
Closest ancestors · 8
Same direction, newest first
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Other references · 76
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Citing papers per year: 2024: 6; 2025: 54; 2026: 71.
131 papers.
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- 2024Large Language Model-Brained GUI Agents: A Surveycited by 162
- 2025Evaluation and Benchmarking of LLM Agents: A Surveycited by 152
- 2025Progent: Securing AI Agents with Privilege Controlcited by 141
- 2025Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agentscited by 125
- 2024GUI Agents: A Surveycited by 103
- 2025Unveiling Privacy Risks in LLM Agent Memorycited by 101
- 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasurescited by 96
- 2025Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agentscited by 62
- 2025LLM Agents for Education: Advances and Applicationscited by 59
- 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 57
- 2025Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attackscited by 49
- 2025Measuring Agents in Productioncited by 37
12 most cited of 86 All 86 (JSON)
- 2026Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents9 Oct 2026
- 2026VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails8 Oct 2026
- 2026Loose Lips: Understanding Privacy Leakage in Agentic Dialogue7 Oct 2026
- 2026Business Compromise Detection with Agentic AI and LLM-driven Knowledge Discovery26 Sep 2026
- 2026AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents25 Sep 2026
- 2026Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges20 Sep 2026
- 2026CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents18 Sep 2026
- 2026When Malicious Instructions Persist: Persistent Memory Poisoning Attack on Harness-Based Agents12 Sep 2026
- 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems1 Sep 2026
- 2026Resource Constraints and Performance in Agentic AI Systems28 Aug 2026
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- 2025Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data25 Sep 2025
- 2025Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction16 May 2025
- 2025Amplified Vulnerabilities: Structured Jailbreak Attacks on LLM-based Multi-Agent Debate23 Apr 2025
- 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment22 Apr 2025
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- 2024When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?20 Oct 2024
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- 2026Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation25 Sep 2026
- 2026From Alignment to Access Control: A Framework for GenAI Policy Enforcement22 Sep 2026
- 2026A Scalable Trust Discovery Architecture for the Internet of Agents17 Sep 2026
- 2026Cross-Corpus Evaluation of Generalizable Vulnerability Detection in IoT Firmware11 Aug 2026
- 2026Beyond Direct Identifiers: Probabilistic Privacy Risk Estimation for Privacy-Conscious LLM Query Delegation10 Aug 2026
- 2026A Security-Oriented Lifecycle Model for Large Language Model Systems4 Aug 2026
- 2026The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems21 Jul 2026
- 2026Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows12 Jul 2026
- 2026IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts6 Jun 2026
- 2026Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network25 May 2026
- 2026Security Risks in Tool-Enabled AI Agents: A Systematic Analysis of Privileged Execution Environments10 May 2026
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