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MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
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Introduces MobileSafetyBench, an Android-emulator benchmark of 250 tasks measuring safety and helpfulness of LLM mobile device-control agents, and proposes Safety-guided Chain-of-Thought (SCoT) prompting.
Problem
No standardized benchmark exists for evaluating the safety of LLM-based mobile device-control agents in realistic interactive environments; prior safety benchmarks rely on question-answering formats.
Approach
Interactive Android-emulator tasks with human-annotated risk severity (low vs high risk), rule-based evaluators on device state, refusal and goal-achievement metrics, plus an SCoT prompt that makes agents generate safety considerations before acting.
Key results
- GPT-4o agents reached 82% goal achievement in low-risk tasks but only 31% refusal in high-risk tasks; Claude-3.5-Sonnet reached 64% refusal in high-risk tasks.
- Under indirect prompt injection, GPT-4o agents defended only 3/50 tasks, Gemini-1.5 8/50, and Claude-3.5 15/50.
- SCoT prompting raised GPT-4o refusal rate in high-risk tasks from 6% to 36% while keeping goal achievement near 82-84% in low-risk tasks.
Surprising findings
- LLMs detect risks in a QA setting (92% for GPT-4o) yet agents in the agentic setting refuse far less (36%), a discrepancy between QA and agentic safety.
- Advanced reasoning (OpenAI-o1) improves refusal but at roughly 4x latency, and o1 agents remain vulnerable to prompt injection.
Limitations
- Risks that are subtle or hard to identify are underrepresented; the benchmark relies on relatively simple embedded risks.
- Limited number of scenarios (100 daily, 10 injection base scenarios) and only 13 apps.
- Single-agent setting; multi-agent and RAG settings not studied.
Experimental setup
Benchmarks: MobileSafetyBench
Metrics: goal achievement rate refusal rate attack defense count risk detection rate
Baselines: GPT-4o GPT-5 Gemini-1.5-Pro Gemini-2.5-Pro Claude-3.5-Sonnet Llama-3.2-90B-Vision Grok-2 Qwen-2.5-VL-72B Pixtral-Large Phi-4 OpenAI-o1 SeeAct
Builds on: Chain-of-Thought ReAct Reflexion M3A SeeAct
Closest papers by meaning
- Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents (NeurIPS 2026 · similarity 0.87)
- LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments (NeurIPS 2026 · similarity 0.87)
- SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION (ICLR 2025 · similarity 0.87)
- GhostEI-Bench: Do Mobile Agent Resilience to Environmental Injection in Dynamic On-Device Environments? (ICLR 2026 · similarity 0.87)
- DIBench: Benchmarking Decision Integrity of GUI-based Mobile Agents Under Deceptive Injections (NeurIPS 2026 · similarity 0.87)
- AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents (ICLR 2025 · similarity 0.86)
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