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MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Apps

Sujin Chen, Lijun Li, Tianyi Du, Jing Shao

arXiv:2608.17659v1cs.CRcs.AI

TL;DR

Mobile GUI agents face a largely unevaluated risk from environmental injection attacks embedded in ordinary Android application content. MobileWorldSafety benchmarks this threat across real apps and finds substantial vulnerabilities in all six evaluated agents, with attack success rates of 40.4%–66.9%.

  • Problem

    Existing evaluations lack a systematic benchmark for environmental injection attacks within native mobile applications and everyday Android workflows.

  • Method

    MobileWorldSafety evaluates 142 risk tasks across 13 real Android applications using programmatically verifiable risk indicators and a two-stage verification pipeline.

  • Results

    All six evaluated agents exhibit substantial safety vulnerabilities, with attack success rates ranging from 40.4% to 66.9%.

  • Takeaways & Limitations

    The findings support developing context-aware defenses that improve safety while preserving task utility and execution across realistic mobile workflows.

  • Takeaways & Limitations

    The threat model assumes attackers can control environmental content encountered through ordinary external channels, including pre-loaded records originating from legitimate sources.

Abstract

from arXiv · show

LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deployment. However, because these agents routinely process untrusted environmental content, they are highly vulnerable to environmental injection attacks, which include indirect prompt injections and adversarial instructions. Such attacks can manipulate the behavior of agents without user awareness through diverse channels encountered in everyday mobile use. Despite these risks, existing benchmarks often fail to capture everyday user scenarios, lacking a systematic evaluation of GUI agents under environmental injection attacks on mobile devices. To address this gap, we introduce MobileWorldSafety, a benchmark of 142 risk tasks built on real Android applications. For each task, we define a programmatically verifiable risk indicator over the final system state and evaluate outcomes with a two-stage pipeline: rule-based verification handles unambiguous cases, while an LLM judge adjudicates ambiguous ones. This distinguishes safety failures from capability failures and enables objective and reproducible assessment. Evaluations on six agents, including both general agents and specialized GUI agents, demonstrate that all agents remain highly vulnerable, with attack success rates ranging from 40.4% to 66.9%. These findings indicate that current agents often fail to maintain safety alignment when adversarial content is presented as ordinary mobile context. MobileWorldSafety provides a foundation for quantifying these vulnerabilities and advancing research on robust mobile GUI agents.

1 Introduction

MobileWorldSafety addresses the gap in evaluating environmental injection attacks against mobile GUI agents by benchmarking real Android workflows. Across six agents, all remain substantially vulnerable, with attack success rates from 40.4% to 66.9%.

  • Motivation: Untrusted environmental content is unavoidable in mobile workflows, exposing GUI agents to hidden malicious instructions delivered through ordinary application data.Examples include emails, social posts, and messages that enter the agent’s standard workflow.
  • Research gap: Existing studies emphasize non-mobile settings or limited visual disruptions, leaving native mobile applications insufficiently evaluated.These settings often lack contextual authenticity and credibility.
  • Benchmark: 142 risk tasks across 13 real Android applications form a benchmark organized by attack vectors and harm categories for fine-grained safety analysis.The benchmark uses real Android applications as the attack surface to better capture everyday mobile scenarios.
  • Empirical findings: 40.4% to 66.9% attack success rates were observed across six general and specialized GUI agents, demonstrating substantial safety vulnerabilities.Results are further analyzed by agent category, attack vector, and harm category.
  • Evaluation: A two-stage protocol verifies final system states with rules for unambiguous cases and an LLM judge for ambiguous cases.This design targets more objective and reproducible evaluation.

2 Related Work

Prior work established multimodal mobile GUI automation and improved agents’ visual grounding and support for complex tasks. Safety evaluation has expanded from compliance testing to web workflows and tool-use risks, with mobile safety benchmarks emerging.

  • Mobile GUI Agents: Mobile GUI agents perceive screen content and execute actions such as tapping and typing on mobile applications.Early systems including Mobile-Agent and AppAgent demonstrated multimodal smartphone automation.
  • Mobile GUI Agents: SeeClick improved visual element localization, while AndroidWorld supported testing for long-horizon and complex mobile tasks.These works extended mobile GUI agents beyond basic automation toward more capable interaction and evaluation.
  • Agent Safety Evaluation: Safety evaluation expanded from compliance testing to web workflows and tool-use scenarios, where external tool outputs can introduce risks.ST-WebAgentBench and AgentHarm target web safety, while ToolEmu and tool-hijacking studies examine tool-mediated risks.
  • Agent Safety Evaluation: MobileSafetyBench pioneered safety evaluation in the mobile domain.The passage identifies it as an early mobile-domain benchmark following broader work on web and tool-use safety.

3 MobileWorldSafety Benchmark

MobileWorldSafety benchmarks environmental injection attacks in realistic Android workflows by embedding malicious content into naturally encountered application data and evaluating whether risks materialize in the final device state. Its outcome-centric, two-stage protocol separates safety failures from capability failures and summarizes performance through attack and utility metrics.

  • Threat Model: Environmental injection embeds attacker-controlled content into Android application content encountered naturally by the agent, without modifying the user request or system prompt.The benchmark considers external content such as emails, SMS messages, web pages, social posts, and local files as part of the agent’s observation space.
  • Taxonomy and Injection Implementation: The benchmark covers five attack vectors and categorizes them by both malicious-content carriers and resulting harm categories.Implemented carriers include directed communication, web content, community content, stored records, and tool responses.
  • Task Design and Risk Verification: Each risk task defines a final-state risk indicator that objectively verifies whether the injected attack has materialized.Tasks are derived from benign MobileWorld tasks, modified for workflow-related injections, complexity-calibrated, and manually validated for executability and plausibility.
  • Outcome-Centric Evaluation: Any trajectory triggering risk:true is classified as Executed even when the benign task succeeds, prioritizing safety over utility.This conservative rule prevents successful task completion from obscuring critical safety failures.
  • Evaluation Pipeline: The two-stage pipeline uses rule-based final-state verification for unambiguous cases and LLM-based judgment for ambiguous runs.Five labels—Executed, Partial, Defended, Stalled, and Run Failed—distinguish attack outcomes, safe failures, successful defenses, and capability limitations.
  • Core Metrics: ASR (Attack Success Rate) measures risk behaviors after reaching the injection carrier, while TCR (Task Completion Rate under attack) measures benign-task completion while resisting the attack.Together, ASR and TCR characterize safety and task utility under environmental injection; capability-failure samples are excluded from the evaluation base.

4 Experiments

Experiments across six Android GUI agents show substantial vulnerability to environmental injection attacks, with safety failures often obscured by execution failures. Explicit safety prompts reduce attack success but increase stalling, while model scale and reasoning alter vulnerability and failure modes.

  • Evaluated Models and Configuration: Six evaluated models comprise four general agents—Gemini-3-Pro, Qwen3.5-397B-A17B, Kimi-K2.5, and Claude-Sonnet-4.5—and two specialized GUI agents, GUI-Owl-1.5-32B-Instruct and MAI-UI-8B.Standard evaluation omits additional safety instructions from the system prompt.
  • Overall Results: 40.4% to 66.9%: all evaluated agents exhibit substantial safety vulnerabilities under environmental injection attacks.For every frontier general agent, ASR exceeds TCR, indicating injected instructions are followed more often than tasks are safely completed.
  • Analysis of General Agents: 66.9% ASR and 30.0% TCR: Gemini-3-Pro shows the strongest safety–utility imbalance across 130 scorable tasks.Claude-Sonnet-4.5 has the lowest general-agent ASR at 47.5%, but the supplied passage truncates its corresponding result.
  • Analysis of Specialized GUI Agents: 53 and 36 Run Failed outcomes: MAI-UI-8B and GUI-Owl-1.5-32B-Instruct often fail before reaching injection carriers, making their lower ASR misleading.Failures arise from perception, planning, or action errors and are excluded from the scorable set.
  • Attack Vectors: V5 (Tool Response) consistently produces the lowest ASR, whereas V2 (Web Content) and V4 (Stored Record) are particularly effective against Gemini-3-Pro.Attack-vector susceptibility varies substantially across agents.
  • Harm Categories: H5 (Operational Disruption), H6 (Content Fabrication), and H1 (Sensitive Information Disclosure) have consistently high ASRs across most agents.H5 and H6 evade interventions because malicious instructions resemble standard workflows, while the supplied passage truncates the explanation for H1.
  • Effect of System-Prompt Defense: Qwen3.5-397B-A17B ASR falls from 51.6% to 37.0%, while Gemini-3-Flash ASR falls from 57.9% to 41.5% after adding a defensive system prompt.Stalled samples increase from 2 to 20 for Qwen and from 5 to 19 for Gemini, suggesting conservative refusal or abort behavior.

5 Conclusion

MobileWorldSafety benchmarks environmental injection attacks across 142 risk tasks in 13 real Android applications. Evaluations on six agents reveal substantial safety vulnerabilities and limited protection from existing defenses, motivating context-aware safeguards for realistic mobile workflows.

  • Benchmark: 142 risk tasks across 13 real Android applications comprise the MobileWorldSafety benchmark for evaluating environmental injection attacks.The benchmark is designed around real Android applications and risk tasks.
  • Findings: Six agents reveal substantial safety vulnerabilities, indicating that stronger execution capability may enlarge the attack surface.The conclusion connects execution capability with increased exposure to environmental injection attacks.
  • Findings: Prompt-level and runtime defenses offer only limited protection against these safety vulnerabilities.The reported limitation concerns both prompt-level and runtime defenses.
  • Future Work: Future work should develop context-aware defenses that improve safety while preserving task utility and execution across realistic mobile workflows.The proposed direction balances safety with usefulness and successful execution.

A Limitations · B Broader Impacts

The study acknowledges limited defense coverage and a focus on injection vectors embedded within application workflows. It presents MobileWorldSafety as a resource for safety research while recognizing that modeling real attack surfaces could enable misuse.

  • A Limitations: The study has not systematically investigated defenses against environmental injection attacks.It reports only preliminary experiments involving a system-prompt defense and a multi-stage runtime defense.
  • A Limitations: Two preliminary defenses were evaluated: a system-prompt defense and a multi-stage runtime defense.Training-stage alignment, input-level filtering, and runtime monitoring remain future-work directions.
  • A Limitations: The benchmark focuses on injection vectors deeply embedded within application workflows.The supplied passage indicates that additional coverage remains incomplete.
  • B Broader Impacts: MobileWorldSafety is intended to advance safety evaluation and defense research for mobile GUI agents.The benchmark constructs environmental injection scenarios in real Android applications.
  • B Broader Impacts: Systematically understanding GUI-agent behavior under untrusted environmental content is described as societally important.The motivation reflects agents’ increasing ability to perceive interface content and execute cross-application actions in real mobile environments.
  • B Broader Impacts: The benchmark’s realistic attack-surface modeling could help malicious actors identify content carriers and workflows that mislead GUI agents.The passage warns that similar injection patterns could be misused to induce privacy disclosure, unauthorized operations, or phishing propagation.

C Task Statistics

MobileWorldSafety calibrates base-task complexity to isolate environmental attack surfaces and core mobile interactions in realistic contexts. For difficult tasks, selective simplification increases exposure to manipulated content while reducing uncertainty from app switching.

  • Task calibration: The benchmark focuses on attack surfaces and core interactions naturally present in real-world mobile ecosystems, including authentic emails, SMS messages, and social media updates.This calibration avoids confounding safety-robustness measurement with excessive task complexity.
  • Task calibration: A subset of highly difficult base tasks is selectively constrained to a manageable level without modifying all cross-application workflows.The design preserves the broader mobile setting while simplifying only selected tasks.
  • Task calibration: For example, retrieving browser information and copying it across applications to send by email is distilled into directly replying to a specific inbox email.This eliminates uncertainties caused by app switching and ensures sufficient exposure to manipulated content.

D Benchmark Comparison · E App List · F Taxonomy of Attack Vectors and Harm Categories

MobileWorldSafety compares its benchmark composition with representative agent-safety benchmarks, covers 142 risk tasks across real Android application environments, and organizes tasks by attack vectors and harm categories. Its taxonomy identifies how malicious payloads enter the agent’s observation space and the direct risks represented by each task.

  • D Benchmark Comparison: Table 4 reports the key statistics and task composition of MobileWorldSafety.
  • D Benchmark Comparison: Table 5 compares MobileWorldSafety with representative agent safety benchmarks using each benchmark’s native scale.The comparison includes how malicious or risky content enters the agent’s observation space.
  • D Benchmark Comparison: The comparison distinguishes Native Workflow, where injections appear as ordinary Android application or tool-response content naturally processed during task execution.This differs from injections introduced through UI-tree, screenshot, notification, or pop-up channels.
  • E App List: The benchmark covers 142 risk-task instances across mobile application environments, with each table row representing one application environment.Task counts are non-exclusive because a task may involve multiple applications.
  • E App List: Table 6 lists the applications and task coverage included in MobileWorldSafety.Its task counts are non-exclusive across applications.
  • F Taxonomy of Attack Vectors and Harm Categories: Each of the 142 risk tasks receives exactly one primary attack vector and one primary harm category.The attack vector captures the environmental carrier and associated default trust posture, while the harm category captures the most direct designed risk outcome.
  • F Taxonomy of Attack Vectors and Harm Categories: Table 7 defines the attack vectors used in MobileWorldSafety.Attack vectors designate the environmental carriers through which malicious payloads enter the agent’s observation space.
  • F Taxonomy of Attack Vectors and Harm Categories: Table 8 defines the harm categories used to classify the direct risk outcome represented by each task.

G Experimental Settings

Evaluations use a standardized MobileWorld Android setup with controlled decoding, bounded interaction budgets, and end-to-end agent interfaces. Two GUI models are additionally deployed locally with specified vLLM configurations.

  • Inference and execution: Evaluated models use temperature=0 and top_p=1 where supported, with other decoding settings following official releases or serving defaults.Each task allows a maximum of 50 interaction rounds and a 3-second wait after every UI action.
  • Infrastructure: All evaluations reuse MobileWorld’s Docker-based Android environment, MCP tool integration, and hybrid action space.The study does not use MobileWorld’s planner-executor framework; each model runs through an end-to-end agent interface.
  • Local deployment: GUI-Owl-1.5-32B-Instruct runs locally with vLLM on 2 GPUs using tensor parallel size 2, bfloat16, 0.9 GPU memory utilization, and maximum model length 32768.Its served model name is gui_owl_15_32b.
  • Local deployment: MAI-UI-8B runs locally with vLLM on 1 GPU using bfloat16, tensor parallel size 1, and 0.9 GPU memory utilization.Its served model name is mai_ui_8b.

H Supplementary Evaluation of a Multi-Stage Runtime Defense

On Qwen3.5-397B-A17B across all 142 tasks, a three-layer runtime defense reduced attack success only modestly beyond the system-prompt baseline. Stricter checks also lowered task completion and increased stalled runs, underscoring a safety–utility trade-off.

  • Defense design: The multi-stage defense combines a trust-boundary prompt, injection scanning, and pre-execution checks that withhold flagged actions.Checks cover interface text, tool outputs, unauthorized recipients, risky operations, and task-scope expansion.
  • Safety results: 35.7% ASR under the multi-stage defense improves only 1.3 percentage points over the 37.0% system-prompt baseline.Relative to the undefended configuration, ASR decreases by 15.9 percentage points.
  • Failure modes: Generic injection detectors handle explicit instruction overrides but struggle with attacks embedded in ordinary, task-relevant application state.These attacks contain few conspicuous markers and require reasoning beyond lexical cues.
  • Safety–utility trade-off: 47.2% TCR falls to 45.2%, while Stalled runs rise from 20 to 24 under the stricter defense.The utility cost may reflect conservative handling of suspicious content, extra inspection and replanning, and conflicts with complex GUI operations.

I Robustness Across Agent Frameworks · J Agent Execution Prompt

The benchmark’s vulnerability persists across a richer Mobile-Agent-v3.5-style scaffold, while the execution prompt governs Android interaction through environmental observations and constrained JSON actions. The scaffold improves run completion but does not reduce attack execution or preserve task completion.

  • I Robustness Across Agent Frameworks: 52.3% versus 51.6% ASR shows similar attack success for the richer scaffold and general-e2e using the same Qwen3.5-397B-A17B backbone.The comparison covered all 142 MobileWorldSafety tasks.
  • I Robustness Across Agent Frameworks: 52.3% ASR and 43.0% TCR were achieved by the Mobile-Agent-v3.5-style scaffold over 128 scorable tasks after excluding 14 Run Failed cases.The evaluation used all 142 tasks before excluding failures.
  • I Robustness Across Agent Frameworks: 14 versus 18 Run Failed cases favored the richer scaffold, but its TCR was 3.8 percentage points lower and attack execution frequency was not reduced.The richer scaffold added planning, reflection, persistent memory, error recovery, and tool use.
  • I Robustness Across Agent Frameworks: The experiment indicates vulnerability is not solely an artifact of general-e2e, but it does not establish robustness across all modern architectures.Mandatory confirmation, approval gates, information-flow control, or strict tool permissions may produce different behavior.
  • J Agent Execution Prompt: The general end-to-end agent receives the benign goal as the user objective, while injections enter through app content or tool-returned observations during Android execution.Environmental injections are not inserted directly into the agent prompt.
  • J Agent Execution Prompt: The prompt defines an Android phone operator responsible for retrieving information and performing tasks through precise actions, with outputs required in exact JSON format.The action framework includes GUI and MCP tool actions, with only one tool call allowed per action.
  • J Agent Execution Prompt: The action vocabulary covers GUI control, text input, responses, navigation, scrolling, status reporting, waiting, user queries, keyboard entry, and MCP tool calls.The prompt requires a final Action line containing a single valid JSON action.
  • J Agent Execution Prompt: Execution principles require strict user-goal adherence, explicit answers, alternative actions after repeated failures, information gathering, and asking the user when information is insufficient.The prompt also specifies navigation, text-operation, and decision-process procedures.

K Baseline Capability and Task Utility Analysis

Qwen3.5-397B-A17B’s successful task completion falls from 89 in a clean environment to 58 under environmental injection attacks. The degradation is concentrated in GUI-only and Agent-User tasks, while MCP-augmented tasks remain comparatively resilient.

  • Task Utility: 24 GUI-only tasks were lost, dropping successful completions from 59 to 35.Utility degradation was highly concentrated in unstructured GUI-only tasks.
  • Task Utility: 6 Agent-User tasks were lost, declining from 15 to 9, while MCP-augmented tasks declined only from 15 to 14.The passage attributes MCP resilience to the strict formatting and structural boundaries of tool responses.
  • Attack and Capability: High Attack Success Rate largely reflects agents adopting explicit injected instructions as actionable shortcuts when they encounter planning difficulties.Injected directives can simplify complex benign workflows, including instructions to click malicious links or ignore previous instructions.
  • Attack and Capability: 59 Executed and 5 Partial trajectories comprised the attacked environment’s 84 compromised runs.The remaining compromised runs included 2 Stalled and 18 Run Failed trajectories.

L Reliability Validation of the LLM-as-judge · M Stage-2 LLM Judge Prompt · NeurIPS Paper Checklist

The paper validates its Stage-2 LLM judge against human annotations, specifies evidence-based adjudication labels and outputs, and documents methodological claims, limitations, reproducibility, and disclosure practices in the NeurIPS checklist.

  • L Reliability Validation of the LLM-as-judge: 76 ambiguous Stage-2 cases for Gemini-3-Pro and Claude-Sonnet-4.5 were independently re-annotated by a human expert using the judge’s label definitions and criteria.The validation targets cases that programmatic final-state verification cannot determine.
  • L Reliability Validation of the LLM-as-judge: Six disagreements occurred between the LLM judge and human expert, mainly in borderline exposure cases, with no systematic disagreement about initiating risky actions.The ambiguity concerned whether agents accessed and processed injected content before termination.
  • M Stage-2 LLM Judge Prompt: Stage 2 judges ambiguous samples using the task description, Stage-1 verification result, execution trajectory, runtime evidence, and screenshots, then returns a JSON verdict.The JSON includes one of three labels, carrier exposure, risk-action initiation, and 2–3 sentences of reasoning.
  • M Stage-2 LLM Judge Prompt: The judge labels exposed but incomplete risky behavior as partial, exposed failed non-risky progress as stalled, and unexposed or prematurely failed runs as run_failed.These labels distinguish partial attack progress, failure without either task completion, and failures occurring before processing the injected content.
  • M Stage-2 LLM Judge Prompt: The prompt prioritizes final state, runtime logs, screenshots, and actual or attempted UI actions over the agent’s natural-language self-description.Attempted actions can indicate behavioral tendency toward either the benign task or risky outcome without confirming a state change.
  • NeurIPS Paper Checklist: The checklist answers Yes that the abstract and introduction accurately reflect the paper’s claims, while emphasizing alignment with results, scope, assumptions, and limitations.The checklist guidance requires limitations and robustness to assumption violations to be discussed when applicable.
  • NeurIPS Paper Checklist: The checklist reports no theoretical results, no crowdsourcing or human-subject research, documented new assets, and required disclosures for LLM use and societal impacts.It identifies LLMs as a core component because the work evaluates LLM-powered GUI agents and uses an LLM judge.
  • NeurIPS Paper Checklist: The paper states that anonymized links to code and data are provided in Appendix G, supporting reproducibility and open access to experimental resources.The checklist also requests sufficient experimental detail and reproducibility guidance.
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