mlsanityData to 14 Oct 2026

ActReal: System-Level Mobile Agents Challenge Mobile Automation Detection

arXiv 2608.30038 ↗First posted 30 Aug 2026cs.CRcited by 0

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

Abstract (arXiv)

System-level mobile agents are evolving from fixed scripts into adaptive systems that continuously observe interfaces, reason, and adjust their actions, allowing automated attacks to navigate dynamic UIs and complete complex tasks. Existing applications detect automation using touch trajectories, action timing, and the physical coupling between touch and inertial measurement unit (IMU) signals. However, a privileged system-level agent executor can control both touchscreen input and application-visible sensor delivery, enabling it to jointly generate time-aligned touch and six-axis IMU signals and evade these defenses. We present ActReal, a physical-action attack framework for system-level mobile agents. ActReal converts semantic agent actions into task-valid touch and IMU events using genuine-trajectory adaptation and physics-guided IMU generation. ActReal achieves a mean event-level attack success rate of 77.5%; even when detectors jointly observe touch and IMU, its attack success rate remains 71.1%.

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 17 of its 35 references that are in the map. First posted on arXiv 30 Aug 2026.

Builds on

17 of 35 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization24 Feb
  2. 2025Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks20 Jan
  3. 2024Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration3 Jun
  4. 2023AppAgent: Multimodal Agents as Smartphone Users21 Dec

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  4. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
  5. 2023AutoDroid: LLM-powered Task Automation in Androidcited by 184
  6. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  7. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Seriescited by 43
  8. 2025From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agentscited by 10
  9. 2023BehaveFormer: A Framework with Spatio-Temporal Dual Attention Transformers for IMU enhanced Keystroke Dynamicscited by 5
  10. 2026Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating Systemcited by 4
  11. 2019Fragility of Layout-Based and Visual GUI Test Scripts: An Assessment Study on a Hybrid Mobile Applicationcited by 2
  12. 2026AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interactioncited by 2
1 more
  1. 2019ZKSENSE: A Friction-less Privacy-Preserving Human Attestation Mechanism for Mobile Devicescited by 1

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  1. 2024MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
  2. 2026PalmClaw: A Native On-Device Agent Framework for Mobile Phones
  3. 2026(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents
  4. 2025Who Grants the Agent Power? Defending Against Instruction Injection via Task-Centric Access Control
  5. 2026AgentRAE: Remote Action Execution through Notification-based Visual Backdoors against Screenshots-based Mobile GUI Agents
  6. 2026ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices
  7. 2025Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents
  8. 2025Agent-SAMA: State-Aware Mobile Assistant