From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents

• URL: http://arxiv.org/abs/2505.12981v2
• Date: Tue, 20 May 2025 07:02:05 GMT
• Title: From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents
• Authors: Liangxuan Wu, Chao Wang, Tianming Liu, Yanjie Zhao, Haoyu Wang,
• Abstract summary: We present the first comprehensive security analysis of mobile large language models (LLMs)<n>We identify security threats across three core capability dimensions: language-based reasoning, GUI-based interaction, and system-level execution.<n>Our analysis reveals 11 distinct attack surfaces, all rooted in the unique capabilities and interaction patterns of mobile LLM agents.
• Score: 17.62574693254363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing adoption of large language models (LLMs) has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex tasks directly on smartphones. However, the security implications of these agents remain largely unexplored. In this paper, we present the first comprehensive security analysis of mobile LLM agents, encompassing three representative categories: System-level AI Agents developed by original equipment manufacturers (e.g., YOYO Assistant), Third-party Universal Agents (e.g., Zhipu AI AutoGLM), and Emerging Agent Frameworks (e.g., Alibaba Mobile Agent). We begin by analyzing the general workflow of mobile agents and identifying security threats across three core capability dimensions: language-based reasoning, GUI-based interaction, and system-level execution. Our analysis reveals 11 distinct attack surfaces, all rooted in the unique capabilities and interaction patterns of mobile LLM agents, and spanning their entire operational lifecycle. To investigate these threats in practice, we introduce AgentScan, a semi-automated security analysis framework that systematically evaluates mobile LLM agents across all 11 attack scenarios. Applying AgentScan to nine widely deployed agents, we uncover a concerning trend: every agent is vulnerable to targeted attacks. In the most severe cases, agents exhibit vulnerabilities across eight distinct attack vectors. These attacks can cause behavioral deviations, privacy leakage, or even full execution hijacking. Based on these findings, we propose a set of defensive design principles and practical recommendations for building secure mobile LLM agents. Our disclosures have received positive feedback from two major device vendors. Overall, this work highlights the urgent need for standardized security practices in the fast-evolving landscape of LLM-driven mobile automation.

Related papers

• Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems [0.0]
  Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols.<n>One such risk is a cascading risk: a breach in one agent can cascade through the system, compromising others by exploiting inter-agent trust.<n>In an ACI attack, a malicious input or tool exploit injected at one agent leads to cascading compromises and amplified downstream effects across agents that trust its outputs.
  arXiv  Detail & Related papers  (2025-07-23T13:51:28Z)
• The Dark Side of LLMs: Agent-based Attacks for Complete Computer Takeover [0.18472148461613155]
  Large Language Model (LLM) agents and multi-agent systems introduce unprecedented security vulnerabilities.<n>This paper presents a comprehensive evaluation of the security of LLMs used as reasoning engines within autonomous agents.<n>We focus on how different attack surfaces and trust boundaries can be leveraged to orchestrate such takeovers.
  arXiv  Detail & Related papers  (2025-07-09T13:54:58Z)
• OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety [58.201189860217724]
  We introduce OpenAgentSafety, a comprehensive framework for evaluating agent behavior across eight critical risk categories.<n>Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms.<n>It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors.
  arXiv  Detail & Related papers  (2025-07-08T16:18:54Z)
• Kaleidoscopic Teaming in Multi Agent Simulations [75.47388708240042]
  We argue that existing red teaming or safety evaluation frameworks fall short in evaluating safety risks in complex behaviors, thought processes and actions taken by agents.<n>We introduce new in-context optimization techniques that can be used in our kaleidoscopic teaming framework to generate better scenarios for safety analysis.<n>We present appropriate metrics that can be used along with our framework to measure safety of agents.
  arXiv  Detail & Related papers  (2025-06-20T23:37:17Z)
• ATAG: AI-Agent Application Threat Assessment with Attack Graphs [23.757154032523093]
  This paper introduces AI-agent application Threat assessment with Attack Graphs (ATAG)<n>ATAG is a novel framework designed to systematically analyze the security risks associated with AI-agent applications.<n>It facilitates proactive identification and mitigation of AI-agent threats in multi-agent applications.
  arXiv  Detail & Related papers  (2025-06-03T13:25:40Z)
• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• AgentXploit: End-to-End Redteaming of Black-Box AI Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentXploit, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentXploit on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• DoomArena: A framework for Testing AI Agents Against Evolving Security Threats [84.94654617852322]
  We present DoomArena, a security evaluation framework for AI agents.<n>It is a plug-in framework and integrates easily into realistic agentic frameworks.<n>It is modular and decouples the development of attacks from details of the environment in which the agent is deployed.
  arXiv  Detail & Related papers  (2025-04-18T20:36:10Z)
• Guardians of the Agentic System: Preventing Many Shots Jailbreak with Agentic System [0.8136541584281987]
  This work uses three examination methods to detect rogue agents through a Reverse Turing Test and analyze deceptive alignment through multi-agent simulations.<n>We develop an anti-jailbreaking system by testing it with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models.<n>The detection capabilities are strong such as 94% accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities when under long attacks.
  arXiv  Detail & Related papers  (2025-02-23T23:35:15Z)
• Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks [88.84977282952602]
  A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs)<n>In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents.<n>We conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-12T17:19:36Z)
• Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents [16.559272781032632]
  We present a systematic security investigation of multi-modal mobile GUI agents, addressing this critical gap in the existing literature.<n>Our contributions are twofold: (1) we propose a novel threat modeling methodology, leading to the discovery and feasibility analysis of 34 previously unreported attacks, and (2) we design an attack framework to systematically construct and evaluate these threats.
  arXiv  Detail & Related papers  (2024-07-12T14:30:05Z)
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents [47.219047422240145]
  We take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms.
  arXiv  Detail & Related papers  (2024-02-17T06:48:45Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.