mlsanityData to 14 Oct 2026

When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents

arXiv 2609.16732 ↗First posted 15 Sep 2026cs.CRcited by 0

On the map: LLM agents and tool use → GUI and mobile agents

Abstract (arXiv)

Mobile agents are increasingly capable of autonomously interacting with mobile applications and performing consequential actions on behalf of users. Effective human oversight of such agents relies on a basic premise: users and agents observe consistent information from the same interface. We show that this premise can be systematically violated. Users perceive mobile interfaces through physical displays and the human visual system, making their observations subject to occlusion and luminance contrast limitations. In contrast, agents consume digital screenshots that may retain such content and accessibility representations that expose nonvisual widget metadata. The same UI state can therefore present materially different information to users and agents, a mismatch we term human-agent UI desynchronization. We investigate whether a repackaged clone of a legitimate APK can exploit this desynchronization to steer an agent toward attacker-designated actions, while remaining fully functional and behaviorally consistent with the original application for human users. We demonstrate that this threat is feasible: perturbations embedded before deployment can induce such deviations without access to runtime user instructions, agent detection or online adaptation. To systematically expose and evaluate this threat, we develop an automated framework that constructs user runtime instruction-agnostic UI desynchronization attacks and realizes them in deployable APKs. We conduct static and dynamic evaluations across five mobile-agent frameworks and three backbone models on 546 tasks involving various applications, achieving average misleading rates of 77.9% and 66.9%, respectively. A complementary questionnaire-based study with 186 participants finds that the visual perturbations used in our attacks are difficult for human users to notice.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use. Builds on 30 of its 40 references that are in the map. First posted on arXiv 15 Sep 2026.

Builds on

30 of 40 references.

Closest ancestors · 14

Same direction, newest first

  1. 2026AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agents22 Apr
  2. 2026GraphPilot: GUI Task Automation with One-Step LLM Reasoning Powered by Knowledge Graph24 Jan
  3. 2026GUI-Eyes: Tool-Augmented Perception for Visual Grounding in GUI Agents14 Jan
  4. 2025Mobile GUI Agents under Real-world Threats: Are We There Yet?6 Jul
  5. 2025AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning2 Jun
  6. 2025GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent22 May
  7. 2025Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment20 Mar
  8. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agents21 Jan
  9. 2024AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation24 Dec
  10. 2024Large Language Model-Brained GUI Agents: A Survey27 Nov
  11. 2024SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents17 Jan
  12. 2023AppAgent: Multimodal Agents as Smartphone Users21 Dec
2 more
  1. 2023CogAgent: A Visual Language Model for GUI Agents14 Dec
  2. 2023Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing15 Nov

Other references · 16

  1. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  2. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  3. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
  4. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  5. 2023AutoDroid: LLM-powered Task Automation in Androidcited by 184
  6. 2024Attacking Vision-Language Computer Agents via Pop-upscited by 116
  7. 2023DroidBot-GPT: GPT-powered UI Automation for Androidcited by 47
  8. 2022Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testingcited by 36
  9. 2024Improved GUI Grounding via Iterative Narrowingcited by 15
  10. 2025SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agentscited by 15
  11. 2025GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?cited by 12
  12. 2025From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agentscited by 10
4 more
  1. 2026Mind the Gap: Action Rebinding Attacks against Android GUI Agentscited by 6
  2. 2018Analyzing Use of High Privileges on Android: An Empirical Case Study of Screenshot and Screen Recording Applicationscited by 2
  3. 2026AgentRAE: Remote Action Execution through Notification-based Visual Backdoors against Screenshots-based Mobile GUI Agentscited by 2
  4. 2026MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Contentcited by 1

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Close in wording, no citation either way

  1. 2026Visual Confused Deputy: Exploiting and Defending Perception Failures in Computer-Using Agents
  2. 2026Not an A11y: How Android Accessibility Exposes Mobile AI Agents to Indirect Prompt Injection
  3. 2026Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
  4. 2026PriMobiBench: Characterizing Visual Privacy Leakage in VLM-Driven Mobile GUI Agents
  5. 2026Alignment Is Local: A Paired Diagnostic for GUI Agents under User-Side Persuasion
  6. 2026(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents
  7. 2026Do GUI Agents Believe Their Eyes? Diagnosing State-Belief Reliance on Pixels versus Structure
  8. 2025Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents