MobileJailBench benchmarks jailbreaks against MLLM mobile agents
Original title: MobileJailBench: Red Queen Jailbreak Benchmarking for Mobile Agents
The 520-task suite spans 14 risk categories and reports 100% ASR on Qwen3.5-122B after dynamic evolution.
Researchers introduce MobileJailBench, an evolving jailbreak benchmark for multimodal mobile agents, built on a sandbox covering more than 20 apps. It includes 520 tasks across three jailbreak paradigms and a stratified adjudication framework. Experiments find current agents especially weak to implicit jailbreaks, with alignment that does not transfer from chat refusal to mobile workflows. Dynamic evolution is reported to reach full attack success on Qwen3.5-122B.
Unconfirmed: The 100% attack success rate is the authors' benchmark result on Qwen3.5-122B, not independently confirmed real-world abuse.
- Tasks cover privacy leakage, device compromise, and phishing among 14 risk categories.
- Dual-Dynamic Evolution combines failure-distilled attacks with weakness-driven benchmark growth.
- Authors say safety alignment stays superficial on dynamic mobile interactions.
- The sandbox supports realistic workflows rather than static conversational prompts.
- Products
- MobileJailBench
- AI models
- Qwen3.5-122B
From the original article
1 min read at arxiv.orgMLLM-based mobile agents are rapidly gaining autonomy in perception, planning, and closed-loop execution in dynamic mobile environments. However, this autonomy also broadens the attack surface and amplifies the risks of malicious misuse, including privacy leakage, device compromise, and large-scale phishing.
By Songping Wang · arxiv.org
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