详细信息
- 来源站点
- ArXiv CS.AI
- 作者
- Rahul Deivasigamani, Sayeda Faatin Alvi, Derqui Andrea, Kaushal Punjabi, Stjepan Picek
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-08-11
摘要
arXiv:2608.08939v1 Announce Type: new 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.
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