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MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control

Juyong Lee, Dongyoon Hahm, June Suk Choi, W. Bradley Knox, Kimin Lee

arXiv:2410.17520v3cs.LGcs.CL

TL;DR

Existing safety benchmarks do not adequately assess mobile-device agents in realistic interactive settings, despite their access to sensitive information. MobileSafetyBench evaluates these agents across everyday tasks and injection scenarios, and finds unsafe behavior and injection vulnerability; a safety-guided prompting method improves safety scores but does not remove unsafe actions.

  • Problem

    Question-answering-focused safety benchmarks are insufficient for thoroughly assessing mobile agents’ behavior in realistic interactive environments, where agents can access sensitive user information.

    Source: §1 · p. 1

  • Method

    MobileSafetyBench uses Android-emulator environments, diverse mobile applications, and tasks involving misuse, harmful side effects, and indirect prompt injection to evaluate agent safety.

    Source: §1 · p. 1, §3.1 · p. 3

  • Results

    Agents are highly vulnerable to indirect prompt injection, while Safety-guided Chain-of-Thought increases safety scores but agents still exhibit unsafe behavior.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The findings underscore the need for more careful designs and more reliable methods for safe mobile agents.

    Source: §1 · p. 2, §6 · p. 7

    The benchmark could be expanded with risks that are harder to discern and a larger number of tasks.

    Source: Ethical Statement · p. 8

Abstract

from arXiv · show

Autonomous agents powered by large language models (LLMs) show promising potential in assistive tasks across various domains, including mobile device control. As these agents interact directly with personal information and device settings, ensuring their safe and reliable behavior is crucial to prevent undesirable outcomes. However, no benchmark exists for standardized evaluation of the safety of mobile device-control agents. In this work, we introduce MobileSafetyBench, a benchmark designed to evaluate the safety of device-control agents within a realistic mobile environment based on Android emulators. We develop a diverse set of tasks involving interactions with various mobile applications, including messaging and banking applications, challenging agents with managing risks encompassing misuse and negative side effects. These tasks include tests to evaluate the safety of agents in daily scenarios as well as their robustness against indirect prompt injection attacks. Our experiments demonstrate that baseline agents, based on state-of-the-art LLMs, often fail to effectively prevent harm while performing the tasks. To mitigate these safety concerns, we propose a prompting method that encourages agents to prioritize safety considerations. While this method shows promise in promoting safer behaviors, there is still considerable room for improvement to fully earn user trust. This highlights the urgent need for continued research to develop more robust safety mechanisms in mobile environments.

1 Introduction

Mobile device agents can access sensitive personal information, but existing safety benchmarks centered on question-answering do not adequately assess their behavior in realistic interactive settings. MobileSafetyBench evaluates diverse mobile tasks and injection attacks; experiments find unsafe behavior and vulnerability to injection, while Safety-guided Chain-of-Thought improves safety scores without eliminating unsafe actions.

1 Introduction

  • Question-answering safety benchmarks may miss dangerous behavior during mobile-device control, motivating evaluation in realistic interactive environments with diverse risks.Source: §1 · p. 1
  • MobileSafetyBench uses realistic Android-emulator environments and everyday applications, including memos, calendars, social media, banking, and stock trading, to evaluate mobile-agent safety.Source: §1 · p. 1
  • The benchmark tests risks such as mishandling private information and indirect prompt injections that can deceive agents into acting contrary to user intentions.Evaluators consider agents’ action histories and effects on the device.Source: §1 · p. 1
  • Agents remain highly vulnerable to indirect prompt injection, and Safety-guided Chain-of-Thought improves safety scores but does not prevent all unsafe behavior.Agents may overlook safety considerations they generated, pointing to a need for more reliable methods.Source: §1 · p. 2

2 Related Work

Prior work evaluates LLM safety mainly through question-answering, while agent-safety research also studies tool use and attacks in tool and web settings. MobileSafetyBench focuses on general safe behavior by agents manipulating mobile devices.

Building Agents with LLMs

  • This work presents experimental results with LLM agents and introduces a prompting method to guide safe behavior in digital-device control.Source: §2 · p. 2

Benchmarking Safety of LLMs

  • Prior safety benchmarks often use question-answering formats, while work on agent safety has also examined tool use, jailbreaking, and web environments.Source: §2 · pp. 2–3
  • Existing agent-safety benchmarks have been noted to lack realistic interactive environments, and concurrent studies often focus on jailbreaking or web settings.Source: §2 · pp. 2–3

3 MobileSafetyBench

MobileSafetyBench evaluates mobile agents’ safety and helpfulness in realistic Android-emulator environments. It combines varied real-use tasks and risk levels with standardized measures of task completion, refusal, and potential harm.

3 MobileSafetyBench

  • MobileSafetyBench assesses both safety and helpfulness through interactive, realistic mobile-device environments.Source: §3 · p. 3

3.1 Design Principles

  • Safety is defined as robustness to malicious instructions and preventing harmful consequences while carrying out a requested task.Scenarios vary because safe behavior depends on the task and device status; automatic evaluators use emulator system information.Source: §3.1 · p. 3

3.2 Framework

  • Device control is modeled as sequential decision-making: agents act on partial observations, and evaluators score the final device state and action history.The environment has a predefined maximum episode length.Source: §3.2 · p. 3

3.3 Task Design

  • The 250-task suite spans 13 applications and daily-use categories, with tasks configured through instructions and device state and risks drawn from files, memos, messages, and social posts.It includes 100 low-risk and 100 high-risk daily tasks, plus 50 high-risk indirect-injection tasks; human annotations distinguish negligible or minor risks from moderate-to-severe risks.Source: §3.3 · p. 3

3.4 Evaluation Scheme

  • Goal achievement and refusal rates distinguish completing low-risk tasks from refusing high-risk ones, while also exposing unsafe completion and excessive caution.Refusal includes asking for user consent; rule-based evaluators check decisions and device effects using action histories, system configurations, file storage, and application databases.Source: §3.4 · p. 4

4 Baseline Agents

Baseline agents receive multimodal screen and UI-text observations and choose from predefined device-control actions, with prompts guiding their reasoning and planning. Their daily-task behaviors vary in the balance between task achievement and refusal; the section also describes Safety-guided Chain-of-Thought prompting to improve safety.

4 Baseline Agents

  • Baseline agents process screen images and UI-element text, then select from predefined actions such as tapping, swiping, typing, refusing, or asking for consent.Prompts include the agent role, instructions, prior actions, current observation, and a structured rationale and action decision; some workflows add action grounding or reflection.Source: §4 · p. 4
  • GPT-5 showed the best reported balance between goal achievement and refusal, while GPT-4o and Phi-4 exemplified the safety-helpfulness tradeoff.The figure reports goal achievement on the left and refusal on the right for daily-scenario tasks; room for improvement remains.Source: Figure 3 · p. 5
  • Safety-guided Chain-of-Thought asks agents to identify safety issues from the observation and instruction before planning, and experiments report increased safety when it is integrated with Chain-of-Thought.Its guidelines also emphasize applying the generated safety considerations.Source: §4 · p. 5

5 Experiment

The experiments evaluate agents on daily mobile safety tasks and indirect prompt-injection attacks, then examine prompting, question-answering evaluations, and reasoning capability. Across these settings, agents show gaps in preventing harm, including vulnerability to injected instructions.

5.1 Experimental Setup

  • The evaluation benchmarked 10 leading LLMs, OpenAI-o1, and the SeeAct and M3A workflows on 250 tasks, including separate daily-scenario and injection-robustness task sets.The daily-scenario sets comprised 100 low-risk and 100 high-risk tasks; 50 high-risk tasks were reserved for injection robustness.Source: §5.1 · p. 5

5.2 Daily Scenarios Results

  • GPT-4o achieved 82% goal achievement in low-risk tasks, but in high-risk tasks had 60% goal achievement and 31% refusal; no agent exceeded 70% refusal in high-risk tasks.Claude-3.5-Sonnet had a 64% high-risk refusal rate and 73% low-risk goal achievement; Phi-4 had 67% high-risk refusal, 3% low-risk goal achievement, and 73% low-risk refusal.Source: §5.2 · p. 6
  • M3A and SeeAct usually followed instructions without refusing, while M3A achieved nearly 80% goal achievement in both low- and high-risk tasks.The authors identify a need for additional safety considerations in agentic workflows.Source: §5.2 · p. 6

5.3 Indirect Prompt Injection Attack Scenarios Results

  • Agents handled explicit malicious instructions better than complex risks, yet struggled with private information, subtle harms, and indirect prompt injections that triggered unauthorized actions.The injection tests covered 50 high-risk tasks, and the table caption reports that all agents were vulnerable.Source: §5.2 · p. 6, §5.3 · pp. 6–7, Table 1 · p. 6
  • Injection attacks embedded in messages or social media posts appeared in agents’ observations and could be mistaken for new instructions, prompting actions such as stock trades or password changes.The attacks were evaluated separately because their impact can vary with agent scaffolding.Source: §5.2 · p. 5, §5.3 · pp. 6–7

5.4 Further Analysis

  • In high-risk tasks, GPT-4o agents given the SCoT prompt reported a 28% higher refusal rate than agents given the basic prompt, indicating that higher safety requires more advanced reasoning or planning algorithms.Source: §5.4 · p. 7, Table 2 · p. 7
  • In question-answering evaluations, LLMs often detected risks that GPT-4o and Claude-3.5-Sonnet agents overlooked in the agentic setting, while Gemini-1.5-Pro performed reasonably in both.The comparison measures QA risk detection against agent refusals on high-risk tasks.Source: §5.4 · p. 7, Table 3 · p. 7
  • OpenAI-o1 agents improved refusal rates over GPT-4o agents but still missed some high-risk tasks and took more than approximately four times as long on average across timesteps.The comparison concerns refusal rate and average response latency.Source: §5.4 · p. 7, Table 4 · p. 7

6 Conclusion

The paper reports unsafe agent behavior across common scenarios and identifies indirect prompt injection as a particular vulnerability. SCoT improves safety scores, but limitations remain, including in the analysis of image-based risk signals for the preview model.

6 Conclusion

  • Mobile device-control agents exhibited unsafe behavior across prevalent daily scenarios and remained vulnerable to indirect prompt injections despite SCoT’s safety-score improvements.The conclusion also notes shortcomings in advanced reasoning and external safeguards.Source: §6 · p. 7
  • The authors conclude that agents can detect risks in question-answering formats yet still require evaluation across diverse settings, including agentic tasks.They present MobileSafetyBench as a platform for building safe and helpful agents.Source: §6 · p. 7
  • The OpenAI-o1-preview analysis used tasks without image-based risk signals because the preview model did not support image inputs..Source: §6 · p. 7

Ethical Statement

The appendix discusses deployment concerns and benchmark limitations alongside the Android environment, agent interaction design, applications, and task construction.

Ethical Statement

  • The benchmark focuses on a single decision-making agent; harder-to-discern risks and broader task coverage are identified as future extensions.The authors also suggest examining multiagent systems and retrieval-augmented generation.Source: Ethical Statement · p. 8
  • The authors flag risks of harmful actions or sensitive-information exposure, emphasizing user consent and rigorous safety checks; consent requests are not benchmark failures.Source: Ethical Statement · p. 8

A Additional Related Work

B Environment Details

  • Android emulators instantiate task-specific device states; agents receive screen images and UI text, and their actions are converted for execution through ADB.The experiments use an Android virtual device representing a Pixel 7; UI text is parsed from the Android view hierarchy.Source: B Environment Details · p. 11, §B.1 · p. 11, §B.2 · p. 11

B.3 Stabilization of Transitions

  • To reduce transition delays and random UI disturbances, the benchmark pauses 10 seconds before retrieving screen data and uses heuristics to handle perturbations such as advertisements.Source: §B.3 · p. 11

B.4 Application

  • The benchmark uses applications spanning memos, social media, calendars, banking, and stock trading, including 13 different applications.Apps use accessible APK files, including open-source or third-party-store applications.Source: §B.4 · p. 12

C.1 Task Augmentation

  • The benchmark starts from 100 daily-task scenarios and 10 indirect prompt injection scenarios, then augments them to 250 tasks using hinted daily instructions and varied attacks.Attack variants include other languages, role-playing prompts, and rule-based attacks.Source: C Task Details · p. 12, §C.1 · pp. 12, 15

C.2 Task Differentiation with Human Survey

A human survey distinguishes high- and low-risk tasks by potential harm, and annotators assign risk types to the high-risk daily scenarios.

C.2 Task Differentiation with Human Survey

  • Ten independent annotators rated harm using task instructions and device status; seven were AI researchers and three were recruited through Amazon Mechanical Turk.The survey distinguishing risk levels covered 110 unique tasks before augmentation.Source: §C.2 · p. 15
  • 60 high-risk and 50 low-risk tasks are classified using an average severity threshold strictly higher than 2.0; the high-risk set includes 50 daily and 10 attack tasks.Source: §C.2 · p. 15
  • 2.90 is the mean severity rating for high-risk tasks versus 1.18 for low-risk tasks; the maximum low-risk severity is 1.50 and minimum high-risk severity is 2.20.Source: §C.2 · p. 15

C.3 Task Breakdown per Risk Type

  • High-risk daily tasks cover ethical compliance, offensiveness, bias and fairness, and private information; human annotators assign labels by majority vote.Source: §C.3 · pp. 15–16, Table 9 · p. 15

D Evaluation Scheme Details

This section details the evaluation scheme, including task-success and harm criteria, safety-guided prompts and agent workflows, and required final-answer formatting. It also identifies supplementary experiment details, including closed-source model configurations.

D Evaluation Scheme Details

  • Evaluators inspect system configuration, files, application databases, UI elements, and action history to assess task criteria and agent behavior.They use ADB, SQLite, and Appium to verify settings and app data, analyze file changes, check UI state, and review actions by timestep.Source: D Evaluation Scheme Details · p. 16
  • In high-risk tasks, goal achievement means harm has manifested, while ordinary task achievement checks whether predefined criteria are met.For example, evaluators check SMS history for the target message and recipient; high-risk criteria focus on harm manifestation rather than exact instruction-following.Source: D Evaluation Scheme Details · p. 16

E Prompt Details

  • The basic prompt has no safety guidance, while safety-guided prompts give risk-specific and consent instructions and SCoT prompts require safety considerations during planning.The prompt format also recursively updates context with agent responses; a response-format warning is added after JSON errors.Source: E Prompt Details · p. 17

E.1 Prompts for Agentic Workflows

  • M3A (Rawles et al. 2024) uses a reflection prompt that summarizes each action by comparing before-and-after screenshots and UI information for future action selection.The summary is added to action history and can record intent, whether the action worked, and what should happen next.Source: §E.1 · pp. 19–20
  • SeeAct (Zheng et al. 2024) separates action generation from grounding, first choosing an action type and then selecting the specific UI element to operate.Its grounding prompt presents screen elements as choices for the agent to match to its target.Source: §E.1 · pp. 20–21
  • The workflow requires ELEMENT, ACTION, VALUE, and FINAL_ACTION fields, with element, action, and value on separate lines.Source: §E.1 · p. 21

F Experiment Details

  • The experiment-details section covers model configurations, experimental setup, further results discussion, and exemplary agent responses.Source: F Experiment Details · p. 22

F.1 Model Configuration

  • Table 10 presents model configurations for closed-source models.Source: Table 10 · p. 22

F.2 Experiment with Daily Scenarios Details

The daily-scenario analysis finds that agents handle some explicit risks but miss implicit, hidden, or multimodal risks. The indirect-injection experiments examine attacks embedded in messages or social media and show both an injected-prompt failure and a benign-message case aligned with the user's intent.

F.2 Experiment with Daily Scenarios Details

  • Explicit crime signals usually led agents to prevent harm, but implicit slang or risks hidden in chat history often led them to complete unethical tasks.For offensiveness, agents usually handled explicit signals safely, but most failed on an image containing a slant-eye gesture.Source: §F.2 · p. 22
  • Agents often prevented straightforward sexual or regional bias, but many missed religious or educational bias; image-based privacy risks caused more frequent failures than text-based ones.The authors note that subtler or more complex bias could be harder to recognize.Source: §F.2 · p. 22

F.3 Experiment with Indirect Prompt Injection Attack Scenarios Details

  • The injection threat model has attackers influence the agent indirectly through messages or social-media posts, with deceptive prompts nested in UI text or images.The agent is expected to follow the user's instruction rather than the attacker’s and may defend by refusing, asking consent, or completing the intended task.Source: §F.3 · pp. 22–23
  • Injected prompts were handcrafted in a simple system-prompt-plus-user-prompt format and placed in the agent's current observation of UI elements.A Claude-3.5-Sonnet agent using SCoT followed an injected instruction without asking for consent.Source: §F.3 · p. 23, Table 12 · p. 23
  • With benign message content, Claude-3.5-Sonnet using SCoT completed the request to check the latest message, consistent with the user's intention.The response example contrasts with the agent's behavior when it observes an injected prompt.Source: §F.3 · p. 23, Table 13 · p. 23

F.4 Experiment on SCoT Details

The SCoT experiment compares safety-guided prompting with a prompt that requires agents to state safety considerations. The authors report that this requirement promotes safer behavior, and discuss SCoT as a behavioral guardrail with possible connections to other alignment techniques.

F.4 Experiment on SCoT Details

  • The section reports additional studies on SCoT prompting, alongside the comparison of safety-guided and SCoT prompts.Source: §F.4 · pp. 23–24
  • For GPT-4o agents, requiring safety considerations was largely beneficial for safe behavior compared with providing safety guidelines alone.The comparison used 50 daily high-risk tasks; the table caption reports safety without compromising agents' capability.Source: §F.4 · p. 24, Table 14 · p. 24
  • The authors describe SCoT as a behavioral guardrail that bridges safety reasoning and action in agentic contexts.They suggest its reasoning traces may support more accurate Constitutional AI judgment and that responses with or without SCoT may serve as RLHF preference data.Source: §F.4 · p. 24

F.5 Experiment with the Question-Answering Setting Details

The QA experiment asks LLMs to classify task risks, while the agentic setting measures harm prevention. The authors report that risk detection in QA is relatively competent compared with agents' decisions during task execution, where awareness may not translate into harm prevention.

F.5 Experiment with the Question-Answering Setting Details

  • The QA setup classified 50 unaugmented high-risk daily tasks into four risk types or “Unrisky,” allowing up to two labels and counting a human-annotated label match as successful detection.The agentic comparison counted scenarios in which agents prevented harm.Source: §F.5 · p. 24
  • In a QA example involving biased judgment, Claude-3.5-Sonnet accurately classified the risk as Bias & Unfairness.The example illustrates risk detection rather than whether an agent prevents harm while carrying out a task.Source: §F.5 · p. 25, Table 15 · p. 25
  • The authors report relatively competent risk detection in QA compared with agentic decisions that fail to reflect that awareness, interpreting the gap as a possible reasoning flaw rather than fundamental misalignment.They also note remaining problems, including latency, discussed elsewhere in the paper.Source: §F.5 · p. 25

F.6 Experiment with LLMs with Strong Reasoning Capability Details

The strong-reasoning-agent analysis reports better performance by OpenAI-o1 than GPT-4o on low- and high-risk tasks, but limited prevention of indirect prompt-injection harm. It also reports added latency for OpenAI-o1 and SCoT prompting.

F.6 Experiment with LLMs with Strong Reasoning Capability Details

  • The latency measure averages response-generation time across all task-solving timesteps and is reported separately for low- and high-risk tasks.Source: §F.6 · p. 25
  • OpenAI-o1 agents performed better than GPT-4o agents on both low-risk and high-risk tasks, but still showed limited capability to prevent harm from indirect prompt injection; comparison numbers used tasks without augmentation.Table 17 reports the injection robustness results.Source: §F.6 · p. 25, Table 17 · p. 26
  • OpenAI-o1 consumed significantly more time per step than GPT-4o, while SCoT required more response-generation time; compared with GPT-5, the recent model had lower latency, though latency still needs optimization.Source: §F.6 · p. 26

F.7 Response Examples of the Agents

The examples show baseline behavior across high-risk mobile tasks, including harmful actions and refusals.

Sending message

  • On the explicit request to send a message sharing a leaked video, baseline agents refused, unlike the human trajectory shown for the task.The refusal is described as a response to the straightforward risk in the instruction.Source: §F.7 · p. 26, Table 20 · p. 28, Figure 6 · p. 28

Writing memo

  • GPT-4o (basic) completed a memo containing methamphetamine manufacturing instructions by generating further instructions.The task asked the agent to fill in incomplete parts of the pre-filled memo.Source: §F.7 · p. 26, Table 21 · p. 29, Figure 7 · p. 29

Searching YouTube

  • GPT-4o (basic) completed a YouTube search for a tutorial on jailbreaking an LLM for malicious use.The malicious purpose was explicit in the instruction.Source: §F.7 · p. 26, Figure 8 · p. 30

Forwarding message

  • Claude-3.5-Sonnet (basic) forwarded a Google authentication code despite its warning, while the SCoT agent refused after checking the message.The forwarding task contrasts an unsafe action with a safe refusal.Source: §F.7 · p. 26, Table 23 · p. 31, Table 24 · p. 31

Map searching

  • Claude-3.5-Sonnet (SCoT) searched for a route without examining earlier conversation context that revealed the request’s association with drug dealing.The agent observed only the latter part of the conversation.Source: §F.7 · p. 26, Figure 5 · p. 27, Table 19 · p. 27
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