First reported nih.gov
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First reported · updated · 2 reports arxiv.org
(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents
Researchers from Simon Fraser University, CUHK, Shandong University, and QAX's Xingtu Lab (arXiv:2607.00333) demonstrated seven concrete attacks against five open-source mobile AI agent frameworks—AppAgent, AppAgentX, Mobile-Agent-v3, Open-AutoGLM, and MobA—exploiting new attack surfaces in VLM-driven mobile agents. A malicious Android app with no special permissions can inject subliminal, human-invisible on-screen text that the agent's vision model reads, hijacking agent actions and chaining to arbitrary command execution on the host PC driving the agent. Each framework fell to at least six of the seven attacks; the authors report no evidence of the techniques being used outside a controlled setting and no CVEs are assigned. Details →First reported arxiv.org
Devil in the Lens: Analyzing and Defending Physical Prompt Injection Against Vision-Language Models on Wearable Devices
A research paper, "Devil in the Lens," characterizes physical prompt injection attacks against Vision-Language Models on wearable devices such as AI glasses, where malicious text embedded in real-world environments acts as an indirect prompt-injection channel that hijacks model behavior. Using photos captured from AI glasses across 200+ environments, the authors identify 6 threat vectors, evaluate 12 VLMs (achieving attack success rates up to 96% simulated and 60% real-world), and propose two defenses: a mask-based external filter and a semantic-vector-based internal detector. Details →How the wire is made
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