Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection
- URL: http://arxiv.org/abs/2604.07831v1
- Date: Thu, 09 Apr 2026 05:32:34 GMT
- Title: Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection
- Abstract summary: We propose a red-teaming setting that overlays safety-aligned and harmless UI elements onto screenshots to misdirect the agent's visual grounding.<n>Our method uses a modular Editor-Overlapper-Victim pipeline and an iterative search procedure that samples multiple candidate edits.<n>Our attacks improve attack success rate by up to 4.4x over random injection on the strongest victims.
- Score: 37.5890320718138
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing red-teaming studies on GUI agents have important limitations. Adversarial perturbations typically require white-box access, which is unavailable for commercial systems, while prompt injection is increasingly mitigated by stronger safety alignment. To study robustness under a more practical threat model, we propose Semantic-level UI Element Injection, a red-teaming setting that overlays safety-aligned and harmless UI elements onto screenshots to misdirect the agent's visual grounding. Our method uses a modular Editor-Overlapper-Victim pipeline and an iterative search procedure that samples multiple candidate edits, keeps the best cumulative overlay, and adapts future prompt strategies based on previous failures. Across five victim models, our optimized attacks improve attack success rate by up to 4.4x over random injection on the strongest victims. Moreover, elements optimized on one source model transfer effectively to other target models, indicating model-agnostic vulnerabilities. After the first successful attack, the victim still clicks the attacker-controlled element in more than 15% of later independent trials, versus below 1% for random injection, showing that the injected element acts as a persistent attractor rather than simple visual clutter.
Related papers
- Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection [40.37588773528427]
We present Repeat-After-Me, a black-box adaptive visual prompt injection attack that can reveal personally identifiable information or make malicious tool calls.<n>In our evaluation, injections optimized on one surrogate retain 43-46% of the original ASR on two commercial victims.<n>In a default OpenClaw Discord deployment, an untrusted user can use a minimally injected image to overwrite TOOLS.md, enabling future sensitive behaviors like remote code execution and secret exfiltration.
arXiv Detail & Related papers (2026-09-03T22:44:45Z) - MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents [74.64265314956441]
We propose a unified black-box attack framework that poisons graph-structured external memory with coordinated text-image evidence.<n>MemVenom achieves strong end-to-end attack success with minimal impact on benign performance, reaching up to 99.15% on GPT-5-family web agents.
arXiv Detail & Related papers (2026-06-09T11:53:25Z) - WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents [117.65855863464863]
Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
arXiv Detail & Related papers (2026-04-14T04:50:35Z) - SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
arXiv Detail & Related papers (2026-02-15T16:09:48Z) - Effective and Stealthy One-Shot Jailbreaks on Deployed Mobile Vision-Language Agents [29.62914440645731]
We present a one-shot jailbreak attack that leverages in-app prompt injections.<n> malicious apps embed short prompts in UI text that remain inert during human interaction but are revealed when an agent drives the UI via ADB.<n>Our framework comprises three crucial components: (1) low-privilege perception-chain targeting, which injects payloads into malicious apps as the agent's visual inputs; (2) user-invisible activation, a touch-based trigger that discriminates agent from human touches using physical touch attributes and exposes the payload only during agent operation; and (3) one-shot prompt efficacy, a stealthy-guided, character-level
arXiv Detail & Related papers (2025-10-09T05:34:57Z) - AgentTypo: Adaptive Typographic Prompt Injection Attacks against Black-box Multimodal Agents [22.88469633141419]
We introduce AgentTypo, a framework that mounts adaptive typographic prompt injection by embedding optimized text into webpage images.<n>Our automatic typographic prompt injection (ATPI) algorithm maximizes prompt reconstruction by substituting captioners while minimizing human detectability via a stealth loss.<n>We also develop AgentTypo-pro, a multi-LLM system that iteratively refines injection prompts using evaluation feedback and retrieves successful past examples for continual learning.
arXiv Detail & Related papers (2025-10-05T15:46:56Z) - WAInjectBench: Benchmarking Prompt Injection Detections for Web Agents [34.909802797979324]
We present the first comprehensive benchmark study on detecting prompt injection attacks targeting web agents.<n>We construct datasets containing both malicious and benign samples.<n>We then systematize both text-based and image-based detection methods.
arXiv Detail & Related papers (2025-10-01T18:34:06Z) - Poison Once, Control Anywhere: Clean-Text Visual Backdoors in VLM-based Mobile Agents [54.35629963816521]
This work introduces VIBMA, the first clean-text backdoor attack targeting VLM-based mobile agents.<n>The attack injects malicious behaviors into the model by modifying only the visual input.<n>We show that our attack achieves high success rates while preserving clean-task behavior.
arXiv Detail & Related papers (2025-06-16T08:09:32Z) - Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena.<n>We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search.<n>We also use ARE to rigorously evaluate how the robustness changes as new components are added.
arXiv Detail & Related papers (2024-06-18T17:32:48Z)
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.