Not an A11y: How Android Accessibility Exposes Mobile AI Agents to Indirect Prompt Injection
- URL: http://arxiv.org/abs/2608.08939v1
- Date: Sun, 09 Aug 2026 22:13:55 GMT
- Title: Not an A11y: How Android Accessibility Exposes Mobile AI Agents to Indirect Prompt Injection
- Abstract summary: We show that adversarial prompts can cause autonomous agents to abandon their original objectives, violate context boundaries, and perform unauthorized device actions.<n>We present a taxonomy of these attacks and discuss the need for zero-trust input validation, dedicated security agents, and strict context isolation within mobile agent architectures.
- Score: 12.408125305560269
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rise of autonomous AI agents represents a major paradigm shift in how users interact with mobile devices. Frameworks such as MobileRun and Mobile-Use can autonomously navigate Android applications and execute complex multi-step tasks. To interpret user interfaces, these frameworks rely primarily on Android accessibility (A11y) trees and secondarily on visual screenshots. In this paper, we demonstrate that this architectural dependence on unsanitized accessibility metadata, together with visual input, introduces a systemic vulnerability to indirect prompt injection. We show that adversarial prompts can cause autonomous agents to abandon their original objectives, violate context boundaries, and perform unauthorized device actions. Our empirical evaluation demonstrates goal hijacking, context drift, and unauthorized actions across visually hidden and fully exposed attack scenarios. In aggregate, MobileRun reaches an attack success rate of 0.822 with Gemma4:31B, while Mobile-Use with Qwen3.6:35B reduces this to 0.150 but does not eliminate context drift or unauthorized actions. These findings reveal that current mobile agent frameworks fail to enforce semantic context boundaries, treating passive environmental text as trusted instructions. Finally, we present a taxonomy of these attacks and discuss the need for zero-trust input validation, dedicated security agents, and strict context isolation within mobile agent architectures.
Related papers
- When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents [31.736012043129495]
We show that users and agents observe consistent information from the same interface.<n>The same UI state can present materially different information to users and agents, a mismatch we term human-agent UI desynchronization.<n>We develop an automated framework that constructs user runtime instruction-agnostic UI desynchronization attacks and realizes them in deployable APKs.
arXiv Detail & Related papers (2026-09-15T07:02:10Z) - (A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents [6.848663048236236]
Third-party mobile agents powered by Vision-Language Models (VLMs) have emerged as a promising paradigm for automating smartphone interactions.<n>In this paper, we summarize key differences between third-party mobile agent apps and general apps when interacting with the environment, analyze the security posture of agents, and identify two unique attack surfaces.<n>We design and implement seven concrete attacks, from subliminal text injection and invisible pixel zone exploitation to screenshot tampering and host PC command injection.
arXiv Detail & Related papers (2026-07-01T02:17:19Z) - Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents [93.19140872946842]
Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments.<n>This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour.<n>We introduce textbfsysname, a benchmark to systematically categorize and attribute harm in real-world web agent systems.
arXiv Detail & Related papers (2026-06-11T14:12:43Z) - MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models [53.09772037247959]
We introduce MIRAGE, a framework that learns continuous latent reasoning representations from visible textual reasoning traces.<n>On AndroidWorld, MIRAGE matches explicit chain-of-thought supervised fine-tuning in the 4B ablation with a 3-5x lower decoded-token budget.<n>On AndroidControl, it improves action grounding while generating over 75% fewer tokens.
arXiv Detail & Related papers (2026-06-03T09:01:24Z) - Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation [54.38271718421492]
Adversarial smuggling attacks exploit the Human-AI capability gap.<n>It encodes harmful content into human-readable visual formats that remain AI-unreadable.<n>We conduct a preliminary exploration of mitigation strategies.
arXiv Detail & Related papers (2026-04-08T11:13:16Z) - AgentRAE: Remote Action Execution through Notification-based Visual Backdoors against Screenshots-based Mobile GUI Agents [18.82273534480229]
Mobile graphical user interface (GUI) agents autonomously control applications and operating systems (OS)<n>We propose AgentRAE, a novel backdoor attack capable of inducing Remote Action Execution in mobile GUI agents using visually natural triggers.<n>Our evaluation reveals that the proposed backdoor preserves clean performance with an attack success rate of over 90% across ten mobile operations.
arXiv Detail & Related papers (2026-03-24T09:51:43Z) - Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating System [30.443894673057816]
We conduct a systematic security analysis of state-of-the-art mobile agents using Doubao Mobile Assistant.<n>We decompose the threat landscape into four dimensions - Agent Identity, External Interface, Internal Reasoning, and Action Execution.<n>We propose Aura - a clean-slate secure agent OS.
arXiv Detail & Related papers (2026-02-11T14:52:27Z) - Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE [64.47951172662745]
Cuckoo Attack is a novel attack that achieves stealthy and persistent command execution by embedding malicious payloads into configuration files.<n>We formalize our attack paradigm into two stages, including initial infection and persistence.<n>We contribute seven actionable checkpoints for vendors to evaluate their product security.
arXiv Detail & Related papers (2025-09-19T04:10:52Z) - Hijacking JARVIS: Benchmarking Mobile GUI Agents against Unprivileged Third Parties [19.430061128447022]
We present the first systematic investigation into the vulnerabilities of mobile GUI agents.<n>We introduce a scalable attack simulation framework AgentHazard, which enables flexible and targeted modifications of screen content.<n>We develop a benchmark suite comprising both a dynamic task execution environment and a static dataset of attack scenarios.<n>Our findings reveal that all examined agents are significantly influenced by misleading third-party content.
arXiv Detail & Related papers (2025-07-06T03:31:36Z) - 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.