mlsanityData to 7 Oct 2026

MissClick: Execution-Aware Adversarial Attacks on Coordinate Generation in GUI Grounding Models

arXiv 2608.03740 ↗First posted 4 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Recent GUI visual grounding models generate screen coordinates as digit-token sequences that are parsed into numerical values and mapped to executable clicks. This generation-to-execution interface creates an attack surface that existing objectives over visual representations or coordinate-token sequences do not explicitly model. Although each coordinate digit is predicted as a token, its spatial effect after parsing depends on decimal position: changing a hundreds-place digit by one shifts the coordinate by 100 units, whereas the same change at the ones place shifts it by one. This mismatch motivates attack objectives that account for both numerical coordinate structure and click execution. Moreover, untargeted and targeted attacks require different objectives because they aim to move the click outside the correct region and into an attacker-specified region, respectively. We propose MissClick, an execution-aware white-box attack that aligns optimization with click-level success conditions. MissClick-U maximizes soft-coordinate displacement for untargeted disruption, while MissClick-T minimizes a place-weighted target-digit loss for targeted redirection. On OS-Atlas and UGround across desktop, web, and mobile platforms, MissClick-U achieves untargeted success rates of 75.07% and 72.93% (+16.62 and +30.72 pp), while MissClick-T achieves targeted success rates of 44.86% and 62.67% (+31.73 and +47.06 pp). Among the evaluated objectives, soft-coordinate displacement performs best for untargeted attacks, whereas place-weighted target-digit optimization performs best for targeted attacks, supporting goal-specific execution-aware objective design.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 25 references that are in the map. First posted on arXiv 4 Aug 2026.

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References · 24

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  4. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
  5. 2023CogAgent: A Visual Language Model for GUI Agentscited by 692
  6. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 584
  7. 2024SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agentscited by 553
  8. 2024Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agentscited by 373
  9. 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agentscited by 370
  10. 2023Are aligned neural networks adversarially aligned?cited by 305
  11. 2024ShowUI: One Vision-Language-Action Model for GUI Visual Agentcited by 226
  12. 2023Image Hijacks: Adversarial Images can Control Generative Models at Runtimecited by 165
12 more
  1. 2024Dissecting Adversarial Robustness of Multimodal LM Agentscited by 118
  2. 2025GUI-Actor: Coordinate-Free Visual Grounding for GUI Agentscited by 89
  3. 2025WebInject: Prompt Injection Attack to Web Agentscited by 48
  4. 2025UFO2: The Desktop AgentOScited by 38
  5. 2025Advancing Sequential Numerical Prediction in Autoregressive Modelscited by 26
  6. 2025Phi-Ground Tech Report: Advancing Perception in GUI Groundingcited by 24
  7. 2025On the Robustness of GUI Grounding Models Against Image Attackscited by 14
  8. 2024Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Modelscited by 12
  9. 2025Environmental Injection Attacks against GUI Agents in Realistic Dynamic Environmentscited by 5
  10. 2025VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulationcited by 4
  11. 2025Teaching Metric Distance to Discrete Autoregressive Language Modelscited by 3
  12. 2025From Imperative to Declarative: Towards LLM-friendly OS Interfaces for Boosted Computer-Use Agentscited by 2

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