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Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments
Guo Gan, Yilun Zhao, Cong Chen, Jinbiao Wei, Tingyu Song, Zheyuan Yang, Lin Fu, Hong Zhou
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Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%
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Summary
The paper introduces ANTRAP, a benchmark designed to evaluate the robustness of Android GUI agents against dynamic runtime anomalies. It proposes a four-layer taxonomy (State, Thinking, Action, Round) with ten subcategories of traps and constructs a dataset of 236 tasks with injected adversarial perturbations. Evaluations of 16 leading models reveal universal vulnerability, with performance degradation across all layers. GRPO training experiments distinguish between environment-learnable anomalies (addressable via training) and reasoning-bottlenecked issues (intrinsic limitations).
Entities (15)
Relation Signals (14)
Claude Sonnet 4.6 → evaluatedin → ANTRAP
confidence 95% · Evaluating 16 leading GUI models... Claude-Sonnet-4.6
GUI-Owl-1.5-32B-Think → evaluatedin → ANTRAP
confidence 95% · Evaluating 16 leading GUI models... GUI-Owl-1.5-32B-Think
GPT-5.4 → evaluatedin → ANTRAP
confidence 95% · Evaluating 16 leading GUI models... GPT-5.4
ANTRAP → evaluates → Android GUI Agents
confidence 95% · ANTRAP, a comprehensive benchmark that injects dynamic perturbations into agent execution trajectories.
ANTRAP → usestaxonomy → Round Layer
confidence 95% · We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round)
ANTRAP → usestaxonomy → Action Layer
confidence 95% · We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round)
ANTRAP → usestaxonomy → Thinking Layer
confidence 95% · We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round)
ANTRAP → usestaxonomy → State Layer
confidence 95% · We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round)
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Abstract
Abstract:GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack systematic evaluation of agent robustness against runtime anomalies. We introduce AnTrap, a comprehensive benchmark that injects dynamic perturbations into agent execution trajectories. We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round) with ten fine-grained subcategories, and develop a construction pipeline that preserves task solvability while introducing realistic adversarial conditions. Evaluating 16 leading GUI models, we reveal universal vulnerability to dynamic anomalies, with even the strongest models suffering significant performance degradation. Furthermore, we conduct GRPO training in both original and adversarial environments to validate our benchmark, separating environment-learnable anomalies from reasoning-bottlenecked ones. Our findings show that while single-step traps at state and action layers are largely addressable through adversarial reinforcement learning, deep contextual traps, like state deadlock, expose intrinsic limitations that cannot be resolved by training in environments with traps alone.
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- Source: https://arxiv.org/abs/2608.24099v1
- Canonical: https://arxiv.org/abs/2608.24099v1
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Are Android GUI Agents Robust Against Runtime Anomalies? ANTRAP: Evaluating Agents in Dynamic Adversarial Environments Guo Gan Z Yilun Zhao Y Cong Chen Z Jinbiao Wei Y Tingyu Song U Zheyuan Yang T Lin Fu Z Hong Zhou ZB Z Zhejiang University Y Yale University T Tongji University U University of Chinese Academy of Sciences Abstract GUI agents often encounter dynamic anoma- lies when deployed on Android devices, from unexpected pop-ups to action misuse, yet ex- isting benchmarks lack systematic evaluation of agent robustness against runtime anoma- lies. We introduce ANTRAP, a comprehensive benchmark that injects dynamic perturbations into agent execution trajectories. We propose a taxonomy organizing real-world anomalies into four layers (State, Thinking, Action and Round) with ten fine-grained subcategories, and develop a construction pipeline that pre- serves task solvability while introducing re- alistic adversarial conditions. Evaluating 16 leading GUI models, we reveal universal vul- nerability to dynamic anomalies, with even the strongest models suffering significant perfor- mance degradation. Furthermore, we conduct GRPO training in both original and adversarial environments to validate our benchmark, sepa- rating environment-learnable anomalies from reasoning-bottlenecked ones. Our findings show that while single-step traps at state and action layers are largely addressable through adversarial reinforcement learning, deep con- textual traps, like state deadlock, expose in- trinsic limitations that cannot be resolved by training in environments with traps alone. 1 Introduction Android agents aim to accomplish complex de- vice operation tasks given instructions from human users (Bai et al., 2024; Chen et al., 2025a; Wang et al., 2025; Gan et al., 2026). Recently-released agentic models have shown remarkable long- sequence planning and execution performance on dynamic benchmarks, including AndroidLab (Xu et al., 2025), AndroidWorld (Rawles et al., 2025), and MobileWorld (Kong et al., 2025). B Correspondingauthor:HongZhou <zhouhong_zju@zju.edu.cn>.Our code will be avail- able at https://github.com/gguogan/AnTrap. Despite these contributions, the robustness of agents against Android runtime anomalies remains largely unexplored. This gap is critical since GUI agents frequently encounter runtime anomalies from two sources that require recovery in real- world mobile execution. First, agents face exter- nal environment perturbations such as application ad pop-ups, system glitches and rendering delays that often disrupt correct task execution (Xu et al., 2026c; Zhang et al., 2025; Xu et al., 2026b). Sec- ond, agents make internal errors themselves dur- ing operation, such as hallucinating GUI elements in reasoning, grounding inaccurately or misusing actions, yet they consistently struggle to recover independently (Yuan et al., 2025; Wu et al., 2025a). Consequently, the ability to recover from both types of anomalies is essential for the real-world deploy- ment of Android agents (Zhang et al., 2026; Zhai et al., 2026; Wu et al., 2025b). However, existing benchmarks either use predefined adversarial static trajectories for single-step testing (Xu et al., 2026c; Wei et al., 2026b), or lack systematic anomaly in- jection in dynamic settings, thus failing to cover the full spectrum of real-world runtime challenges we discussed (Sun et al., 2026; Gong et al., 2026). To bridge this gap, we propose ANTRAP, a novel benchmark suite designed with challenging dy- namicANdroidTRAPs to assess agent recovery capabilities against external environmental pertur- bations and internal errors. We construct an orig- inal set of 236 tasks within a dynamic environ- ment extended from AndroidWorld (Rawles et al., 2025). To systematically test the robustness of agents against runtime anomalies, we conduct a pilot study in real-world scenarios and introduce a comprehensive taxonomy for granular adversarial evaluation on Android. As detailed in Figure 1, it consists of four primary layers containing state, thinking, action, and round based on the execu- tion loop principle, along with ten specific subcate- gories for constructing adversarial tasks. arXiv:2608.24099v1 [cs.AI] 25 Aug 2026 Whentryingtoadjustthealarmvolumeviatheslider,agent keepsmissingthetargetduetoa groundingerror. Scenario I: Error occurs in agent action. Whilebookinga hotel,theagentismisledbya full-screenpop- upwhichappearedonthescreen. Scenario I: Full screen pop-up occurs in screen. Whenenteringa healthapp,a shake-to -jumpadredirectstoa fooddeliveryapp, causingtheagenttolosedirectioninthetask. Waiting... Scenario IV: Accident occurs in a long sequence. Whenaddinga workphonenumber,thereasoningfailedtorecognizethe currentsettingwasstillmobileratherthanwork,whichmisledtheaction. ❗pop-up ❗another app ❗error reasoning ❗grounding error Scenario I: Visual interpretation error in reasoning. Can GUI agents recover from these anomalies? Figure 1: Four scenarios of runtime anomalies in Android agent tasks from our pilot study, including pop-ups, reasoning errors, grounding errors, and long-sequence accidents. ANTRAP taxonomy organizes such anomalies into four layers (State, Thinking, Action, Round) and ten subcategories. On ANTRAP, we conduct extensive experiments, evaluating 16 agentic models from 7 organizations known for their leading performance in GUI tasks. Our experiment results reveal that current agents generally exhibit performance drops when facing adversarial perturbations under dynamic traps. We attribute this to the agents’ inadequate integration of historical trajectory context, and a scarcity of adversarial training environments and data. Consequently, to analyze the unique challenges presented by ANTRAP, we conduct online Group Relative Policy Optimization (GRPO) in both the original and adversarial environments. The result- ing performance separates environment-learnable challenges from reasoning-bottlenecked ones: state and action perturbations are largely recovered through adversarial training, whereas multi-step contextual traps such as execution loops resist im- provement even under dedicated training. The contributions are summarized as follows: • We introduce a comprehensive taxonomy of run- time anomalies for Android GUI agents, cate- gorizing real-world deployment challenges into four layers and ten subcategories. •We design the ANTRAP suite, which includes an online environment and a benchmark with dy- namic traps to evaluate agent performance under runtime anomalies. •We analyze current agents using ANTRAP, re- vealing their vulnerability to runtime anomalies. •We conduct GRPO experiments that provide in- sights for future agent development by distin- guishing between environment-learnable chal- lenges and reasoning-bottlenecked issues. 2 Related Work 2.1 Benchmarks for Mobile Usage Benchmarks for GUI agents generally fall into static and dynamic categories as shown in Table 1. Static benchmarks (Rawles et al., 2023; Zhang et al., 2024; Li et al., 2024; Lu et al., 2025) fo- cus on single-step operations and grounding accu- racy, neglecting the capacity of agents for long- sequence tool utilization within dynamic environ- ments. Dynamic benchmarks including Android- Lab (Xu et al., 2025), AndroidWorld (Rawles et al., 2025), and MobileWorld (Kong et al., 2025) con- struct interactive frameworks with foundational An- droid emulators, which assess agent performance through continuous task execution within a live op- erating system (Wu et al., 2026). However, these benchmarks primarily evaluate the overall perfor- mance of mobile agents under stable and clean con- ditions while overlooking their robustness. Some works have acknowledged this issue, but they either focus solely on visual noise (Zhao et al., 2025b; Xu et al., 2026c) or the ambiguity of instructions (Sun et al., 2026), lacking a systematic evaluation of agents’ recovery capabilities when facing runtime anomalies. To address this, ANTRAP introduces a granular taxonomy of runtime anomalies which cover both external environment perturbations and agent internal error cases. 2.2 Agent Robustness to Anomalies Across diverse domains, evaluating and improving the robustness of agents against anomalies has be- come an important research direction. Literatures BenchmarkOnline EnvStochastic TasksIntervention Task Target Computer Agentic Systems ScreenSpot Pro (Li et al., 2025)✗−N/A Grounding WebArena (Zhou et al., 2024)✓✗N/A General Execution OSWorld (Xie et al., 2024)✓✗N/A General Execution It’s a TRAP! (Korgul et al., 2025)✓✗Dynamic Safety AutoElicit (Jones et al., 2026)✓✗Dynamic Safety LPS Bench (Chen et al., 2026)✓✗Dynamic Safety Robustness ScreenSpot (Zhao et al., 2025a)✗−Static Robustness Mobile Agentic Systems ScreenSpot V2(Wu et al., 2024)✗−N/A Grounding AITW (Rawles et al., 2023)✗−N/A General Execution AndroidControl (Li et al., 2024)✗−N/A General Execution GUI Odyssey (Lu et al., 2025)✗−N/A General Execution AndroidLab (Xu et al., 2025)✓✗N/A General Execution AndroidWorld (Rawles et al., 2025)✓N/A General Execution MobileWorld (Kong et al., 2025)✓✗N/A General Execution MobileBench-OL (Wu et al., 2026)✓✗Dynamic General Execution AmbiBench (Sun et al., 2026)✓✗Dynamic Ambiguity SMAN Bench (Xu et al., 2026c)✗−Static Robustness ANTRAP (Ours)✓Dynamic Robustness Table 1: Comparison of recent graphical user interface benchmarks. Unlike prior evaluation suites that predominantly focus on general execution or passive robustness, ANTRAP injects runtime anomalies during execution to assess agent robustness in mobile environments with stochastic instructions. on computer usage (Jones et al., 2026; Zhao et al., 2025a; Wei et al., 2026a) evaluate agent robustness against anomalies in desktop environments across diverse dimensions, such as visual (Chen et al., 2025c), semantic (Korgul et al., 2025), and privacy aspects (Chen et al., 2026). In coding domains, RoTBench (Ye et al., 2024) evaluates the robust- ness of agents against dynamic perturbations, par- ticularly focusing on noisy execution environments and tool interfaces. However, in mobile scenarios, relevant benchmarks (Xu et al., 2026c; Sun et al., 2026; Gong et al., 2026; Wu et al., 2026) are either static or dynamic but only focus on limited anoma- lies as we mention in Section 2.1. Several meth- ods focus on robust execution (Zhang et al., 2026). Methods like Agent-R (Yuan et al., 2025) enhance general agents by pre-collecting error recovery trajectories for supervised fine-tuning, while the mobile-scenario method GUI-Reflection (Wu et al., 2025a) focuses on improving agent recovery by identifying errors, undoing actions, and getting back on track. However, these methods only cap- ture random errors and accidents, lacking control- lable environments and systematic analysis across different anomaly types. Our work fills this gap by injecting fine-grained exogenous environmental and endogenous cognitive perturbations in a dy- namic manner, specifically designed to evaluate the recovery capabilities of agents. 3 Android Trap In this section, we first present the preliminary for benchmark design (Section 3.1). Second, we in- troduce taxonomy of dynamic traps (Section 3.2). Then, we present ANTRAP construction including original task curation and adversarial traps injec- tion (Section 3.3). Finally, we provide data analy- sis including benchmark statistics and human vali- dation for the reliability of ANTRAP. (Section 3.4). 3.1 Preliminary Task Formulation. We propose ANTRAP, a benchmark designed to assess the robustness of GUI agents against runtime anomalies. A conven- tional GUI task expects an agent to fulfill a user instruction via an execution loop of multiple in- teraction roundsR = (S,T,A), comprising the environment-provided state S, the agent’s internal thinkingT, and the executed actionA. However, real-world execution frequently involves dynamic anomalies including environmental disturbances and agents’ own errors. Formally, to simulate these anomalies, we inject dynamic perturbationsL trap into the execution loop at stept, converting clean environments into adversarial ones as follows: X L trap −→ ̃ X, where X ∈S t ,T t ,A t ,R t−k:t , (1) where ̃ X denotes the perturbed counterpart ofX, k is the window size of consecutive steps. Trap Toolbox <S, T, A> origin in execution We should click on the deliver food to search for our target... We should click on the search box now to search for McDonald's.... a. State-layer: Pop-upwindows,fullscreenadvertise... b. Thinking-layer: visionhallucination,reasoningerror... c. Action-layer: Groundingerror,wrongactiontype... d. Round-layer: stuckin loop,contextdisruption... Trap.apk MLLM ADB ... We should click on the deliver food to search for our target... <SENDING TO AGENT WITHOUT A&T IN THIS STEP> S trap We should click on the deliver food to search for our target... We should click on the search box now to search for McDonald's.... We should click on the deliver food first, after that search ... R trap Trap Category Clean Environment with emulators Parallel environments 236 original tasks Tasks with stochastic parameters Sparsesimulator accidents Uncontrollableagent error Original Environment Trap Type Injection Timing Trap Frequency Content variation Trap Environment Fine-grained robustness 0 – max_step Probability 0% –100% Generation or Template abcd ③ Inject trap OpenCV Hook (a) Original Environment (c) Diversity Settings (b) Trap Construction ① We should click on the deliver food to search for our target... We should click on the search box now to search for McDonald's.... A trap We should click on the deliver food to search for our target... We should choose the food category to order a delivery... T trap Dynamic execution ②Call toolbox Figure 2: Overview of ANTRAP. (a) Original Environment: a base emulator that supports parallel evaluation over base tasks and environments. (b) ANTRAP Construction: during dynamic execution, we intercept the agent’s ⟨S,T,A⟩loop and inject four-layer anomalies through the trap toolbox. (c) Diversity Settings: trap type, injection timing, frequency, and content source can be flexibly configured to create diverse and realistic dynamic traps. Prerequisite and Desiderata.Evaluating robust- ness against runtime anomalies introduces two chal- lenges not addressed by existing benchmarks (Xu et al., 2026c). First, the evaluation suite requires fine-grained, category-level diversity to reflect a broad range of real-world anomalies which agents encounter upon deployment, rather than relying on sparse anomalies incidentally triggered within stan- dard dynamic environments. Second, constructing these adversarial conditions requires a systematic and scalable methodology so that each injected per- turbation remains controllable and aligned with the intended evaluation signal. To realize these, we in- troduce a taxonomy of runtime anomalies that trap GUI agents. Subsequently, we detail an injection pipeline that systematically constructs scenarios with traps for each taxonomy category to ensure expansive task diversity. 3.2 Taxonomy of Dynamic Traps Through a pilot analysis of agents’ trajectories col- lected from real-device deployment tasks as de- scribed in Appendix B, our taxonomy STAR con- sists of four layer-based categories: State, Think- ing, Action, and Round layers based on the GUI agent execution loop. As shown in Figure 1 and Table 2: (1) State LayerS trap focuses on one-step anomalies at the state level, including unexpected pop-up windows, rendering errors, and transient UI glitches; (2) Thinking LayerT trap focuses on errors at the thinking level, such as temporal misalign- ments and visual hallucinations; (3) Action Layer A trap focuses on issues in action selection, includ- ing grounding errors, action type errors, and incon- sistencies between actions and thinking; (4) Round LayerR trap indicates errors spanning several steps, like long-time deadlocks, system interruptions, and falling into loops. These four categories are further divided into ten subcategories. 3.3ANTRAP Construction Original Task Curation. We use Android- World (Rawles et al., 2025) as our base clean en- vironment due to its dynamic nature and extensive usage. To enhance the diversity of the baseline tasks, we manually augment the original suite of 116 tasks by introducing new scenarios within this environment, yielding a total of 236 tasks with stochastic variations, all of which undergo metic- ulous manual checking to ensure their operational feasibility and technical soundness. Specific details are provided in the Appendix A. Dynamic Trap Construction. To simulate au- thentic dynamic anomalies, we build a runtime pipeline that injects traps during agent execution, guided by the STAR taxonomy. Specifically, as shown in Figure 2, for state trapsS trap , we di- rectly modify the emulator’s state. This is achieved by applying image-level edits via native UI ele- ments on screenshots or broadcasting a customized SubcategoryDefinition Category 1: State Layer (1) External InterruptionUnexpected windows, system alerts or applica- tion jumps. (2) Visual ObscurationRendering errors or image loading failures ob- scuring critical interface. Category 2: Thinking Layer (3) Temporal ConflictFalse or outdated observations that lead the agent to reason about a misaligned state. (4) Visual HallucinationHallucinate text, icons, buttons, relationships, or details that do not exist on the screenshot. Category 3: Action Layer (5) Grounding ErrorExecuted action coordinates deviating from the intended target UI components. (6) Type MismatchMisuse of action types, including confusing click, long press, and double-click; or calling undefined tools in action space. (7) Intent DeviationExecuted actions diverging from the generated internal reasoning and planning logic. Category 4: Round Layer (8) State DeadlockAn unresponsive interface where environment state updates stall completely despite actions. (9) Context DisruptionSudden desktop jumps or unrelated application switches breaking execution. (10) LoopLoop in a repeated state cycle. Table 2: Systematic taxonomy of dynamic traps in the ANTRAP benchmark. Comprehensive programmatic implementation details are provided in the Appendix A. APK that triggers pop-ups, which support both pre- defined templates and real-time LLM-generated contents. The changed observations are recorded in the agent’s history. For thinking trapsT trap , we substitute single-step observations with incor- rect ones to induce hallucinations or temporal con- flicts, simulating cognitive challenges. Notably, the changed observation is excluded from the history, while the agent’s induced output is permanently logged. Consequently, the agent must navigate sub- sequent states burdened by its own hallucinated context, which necessitates autonomous recovery from internal thinking errors. For action traps A trap , we intercept the agent’s output to dynami- cally override coordinates or action types before execution, with the edited physical actions logged. For round trapsR trap , we hijack the true execu- tion flow across multiple steps, utilizing predefined trajectories or runtime execution history segments to disrupt multi-step reasoning and execution. The entirety of these prolonged disruptions is recorded in the history. All the implementation adheres to two principles. First, we enforce dynamic inter- vention, which means perturbations are injected stochastically along the trajectory rather than ap- plied to static trajectories. Second, we guarantee solvability preservation, indicating that the injec- tion never renders the user instruction impossible to complete, so that any performance degradation is attributable solely to the agent’s inadequate robust- ness to runtime anomalies. Full implementation details are provided in the Appendix A. 3.4 Data analysis Statistics.Our benchmark pairs the 236 original tasks with their adversarial counterparts, which all have stochastic elements in instructions and envi- ronments. The benchmark supports the evaluation of all four-layer and ten-subcategory anomalies across every task, enabling a granular assessment of agent robustness. We additionally expose con- figurable parameters for trigger time and injection frequency, allowing users to adjust task difficulty as detailed in Appendix A. Human Validation.To validate the reliability of ANTRAP, we conduct a human study on all tasks across ten subcategories. Expert annotators are in- structed to verify three criteria: (1) the solvability of the original task, (2) the preserved solvability af- ter perturbation injection, and (3) the validity of the trap (i.e., whether it plausibly occurs in real mobile environments). Across the validated sample,91% of tasks satisfy all three criteria. Failing tasks are revised before inclusion. 4 Main Experiments 4.1 Experiment Setup Metrics in Evaluation. Following previous work (Xu et al., 2025; Rawles et al., 2025), we use task rule-based success rate as the evaluation met- ric with Pass@3 and unified settings as detailed in Appendix A. We also demonstrate the performance drop compared to the original environment without traps to reflect the robustness against perturbations. Evaluated Models. We evaluate a wide range of multimodal models which possess agent capa- bilities as follows: (1) Thinking Models includ- ing Gemini-3-Pro (Google, 2025), Claude-Sonnet- 4.6 (Anthropic, 2026), GPT-5.4, and GPT-5.4- Mini (OpenAI, 2026). UI-TARS-1.5-7B (Qin et al., 2025); MAI-UI (Zhou et al., 2025), for which we in- clude 2B and 8B versions; GUI-Owl-7B (Ye et al., 2025); GUI-Owl-1.5-Think (Xu et al., 2026a), for which we include 32B and 8B versions; Qwen3- VL-Thinking (Team, 2025), for which we include 4B and 8B versions. (2) Instruct Models includ- ing GUI-Owl-1.5-Instruct (Xu et al., 2026a), for which we include 8B and 32B versions; Qwen3- VL-Instruct (Team, 2025), for which we include ModelOrig S-LayerT-LayerA-LayerR-Layer Avg Ext.I. Vis.O.Tmp.C. Vis.H.Grd.E. Typ.M. Int.D.St.DL. Ctx.D. Loop Human Baseline Human Annotator94.194.192.4--94.193.6-93.292.8-93.4 Proprietary Models Claude-Sonnet-4.674.264.866.166.972.069.967.869.563.163.161.966.5 Gemini-3-Pro72.9 61.968.660.262.768.660.666.163.661.462.363.6 GPT-5.465.355.153.857.664.8-61.961.950.052.551.756.6 GPT-5.4-Mini42.831.425.040.329.7-29.233.131.834.739.432.7 Open-Source Instruct Models GUI-Owl-1.5-32B-Instruct68.258.162.757.265.363.164.066.163.662.761.062.4 GUI-Owl-1.5-8B-Instruct64.8 55.166.958.165.365.763.160.255.157.258.560.5 Qwen3-VL-4B-Instruct54.737.752.147.931.450.850.849.242.843.252.145.8 Qwen3-VL-8B-Instruct52.138.153.841.547.049.650.048.341.545.347.946.3 Open-Source Thinking Models GUI-Owl-1.5-32B-Think69.560.267.852.567.467.465.765.355.554.268.262.4 GUI-Owl-1.5-8B-Think66.153.066.558.560.267.462.363.663.157.265.361.7 GUI-Owl-7B63.143.257.640.758.959.355.557.242.426.335.647.7 MAI-UI-2B33.527.531.826.734.330.926.327.529.225.830.129.0 MAI-UI-8B53.4 40.351.747.054.252.550.051.350.044.545.848.7 Qwen3-VL-4B-Thinking57.245.350.041.150.050.853.855.547.943.246.648.4 Qwen3-VL-8B-Thinking62.759.361.048.761.964.461.060.254.258.936.956.6 UI-TARS-1.5-7B29.728.027.121.622.917.821.221.223.713.620.821.8 Table 3: Success rates (%) of GUI agents on original tasks and under each trap subcategory of ANTRAP. Cell color intensity indicates performance drop from the original baseline. Abbreviations: Ext.I. = External Interruption, Vis.O. = Visual Obscuration, Tmp.C. = Temporal Conflict, Vis.H. = Visual Hallucination, Grd.E. = Grounding Error, Typ.M. = Type Mismatch, Int.D. = Intent Deviation, St.DL. = State Deadlock, Ctx.D. = Context Disruption. 4B and 8B versions. Notably, there are some spe- cial considerations regarding the inference method of GPT models and thinking-layer trap design for instruct models as detailed in Appendix A. Baseline and Human-level Performance. We utilize original environment as baseline without traps to evaluate agent robustness based on perfor- mance degradation. We also assess human perfor- mance on ANTRAP. Human annotators receive prior training and complete tasks relying solely on visual screen observations and environmental in- teractions. To prevent data leakage from identical original set, we randomly assign tasks without per- turbation hints and ensure each evaluator completes exactly one task per subcategory. Note that some categories are not applicable to humans. 4.2 Experiment Results ANTRAP presents substantial challenges for cur- rent agentic models.The results reveal a univer- sal vulnerability among current GUI agents when exposed to runtime anomalies. Across all evalu- ated models, ranging from proprietary models to open source ones, the average success rate under trapped conditions consistently falls below the orig- inal baseline performance. Even the most capable models, such as Claude-Sonnet-4.6 and GUI-Owl- 1.5-32B-Think, experience notable performance degradations, dropping from 74.2% to 66.5% and 69.5% to 62.4% respectively. This uniform decline shows that existing agents are primarily optimized for ideal execution paths and lack the robustness required to deal with unexpected anomalies. Models struggle more with contextual anomalies than single-layer ones. The four trap layers dif- fer markedly in difficulty. Based on the subcategory results in Table 3, most models experience a signif- icant performance degradation under R-layer traps. Conversely, their performance is relatively better preserved under single-step anomalies, including A-layer traps, Visual Obscuration in S-layer, and Visual Hallucination in T-layer. This observation suggests that models can tolerate simple, immedi- ate disturbances better but struggle with complex scenarios requiring cross-step reasoning and long- term dependencies, including Round-layer tasks and Temporal Conflicts in T-layer. Additionally, the substantial performance drop caused by Ex- ternal Interruption indicates a weak capacity for addressing unexpected events. We hypothesize that this vulnerability stems from a lack of unexpected Original S-Layer A-Layer T-Layer R-Layer 40 60 Success Rate (%) 63.1 50.4 57.3 49.8 34.8 69.9 52.1 58.9 50.8 35.4 69.5 60.4 64.0 54.9 36.8 GUI-Owl-7B Baseline +Orig-GRPO +AnTrap-GRPO Original S-Layer A-Layer T-Layer R-Layer 20 30 40 Success Rate (%) 29.7 27.6 20.1 22.2 19.4 36.0 28.8 22.2 23.1 20.1 35.6 35.8 27.0 26.5 21.2 UI-TARS-7B Baseline +Orig-GRPO +AnTrap-GRPO Figure 3: Layer-level performance comparison across training regimes. Grouped bars show the results of baseline, original-env GRPO, and ANTRAP-env GRPO for each trap layer. anomaly data during model training, as well as the agents’ tendency to prioritize the current observa- tion over long-horizon historical context, which we further explore more in Section 5. Reasoning capabilities do not equate to robust- ness.While reasoning mechanisms raise the gen- eral performance in clean environments, they do not improve robustness against runtime anoma- lies. As illustrated in Table 3, thinking models establish higher original baselines than their stan- dard instruct versions. For example, Qwen3-VL- 8B-Thinking achieves an original success rate of 62.7% compared to the 52.1% of its instruct ver- sion. However, when evaluated in the ANTRAP, the absolute magnitude of performance degrada- tion for thinking models remains comparable to, and even occasionally exceeds, that of instruct models. Quantitatively, Qwen3-VL-8B-Thinking drops 6.1 points under traps (62.7%→56.6%), es- sentially matching its Instruct counterpart’s 5.8- point drop (52.1%→46.3%). GUI-Owl-1.5-32B- Think loses 7.1 points (69.5%→62.4%), more than the 5.8 points lost by GUI-Owl-1.5-32B-Instruct (68.2%→62.4%). This indicates that current think- ing paradigms alone are insufficient to bridge the fundamental gap in robustness. 5 Exploring Strategies to Enhance GUI Agents Robustness In this section, we conduct Group Relative Policy Optimization (GRPO) experiments in both the orig- inal and the adversarial environments to investigate how RL in different environments influences GUI agent performance. We first present preliminary knowledge of GRPO (Section 5.1). Then we intro- duce the experiment details of RL in both the orig- inal environment (Section 5.2) and the ANTRAP environment (Section 5.3). 5.1 Preliminary GRPO is widely utilized to train agents on trajectory-level in dynamic environments. For- mally, for a given task, the agents rollout a group of Gtrajectories, which are subsequently evaluated by a reward function to determine their quality. GRPO optimizes the policy by deriving advantage val- ues through relative comparisons among the group without a separate critic model. The simplified GRPO policy gradient objective in training is for- mulated as shown in Equation 2: J GRPO (θ) =E h 1 G P G i=1 1 |o i | P |o i | t=1 ρ i,t (θ) A i i (2) whereρ i,t (θ) = π θ (o i (t)|o i,<t ) π old (o i (t)|o i,<t ) is the token-level probability ratio for thet-th token in completion o i given its preceding contexto i,<t . This ratio is practically clipped byεto bound policy updates. Because the environment feedback is evaluated at the trajectory level, all tokens within the same com- pletiono i share a uniform sequence-level relative advantageA i , bypassing fine-grained token-wise return estimations. The advantage normalizes the trajectory rewards:A i = r i −mean(r) std(r) , wherer i is the individual sequence reward andrdenotes the reward set of the sampled group. Following this paradigm, we conduct GRPO training on UI- TARS-1.5-7B and GUI-Owl-7B in both the orig- inal and the adversarial environment. This com- parative setup allows us to systematically observe how GRPO in different environments influences the general performance and the dynamic robust- ness against runtime anomalies of the agents. 5.2 GRPO in Original Environment We train UI-TARS-1.5-7B and GUI-Owl-7B using sequence-level GRPO in the original environment without any injection. For each task, we sample G = 8rollout trajectories and assign a binary re- ward based on rule-based task completion. More experiment details are provided in Appendix A. Results on Original Tasks.As shown in Table 4 and Figure 3, GRPO in the original environment yields consistent improvements on standard task completion. GUI-Owl-7B improves from 63.1% to 69.9%, and UI-TARS-7B from 29.7% to 36.0%, confirming that online RL effectively enhances gen- eral task execution capabilities. Results on Adversarial Tasks. The robustness gains against runtime anomalies are marginal. As shown in Figure 3, at the layer level, A-layer ex- hibits some improvement (+1.6% for GUI-Owl, +2.1% for UI-TARS). We attribute this to the better action execution patterns learned during standard training, which offer implicit robustness to action- level perturbations. S-layer receives less benefit (+1.2%∼1.7%), while T-layer and R-layer gain negligible improvement (under +1.0% across most subcategories). These results suggest that while reinforcement learning in the original environment improves the agent’s baseline performance, it fails to strengthen the robustness and recovery capabili- ties needed to handle runtime anomalies. 5.3 GRPO in ANTRAP Environment We conduct ten independent GRPO runs, each ded- icated to a single trap subcategory. For each run, training tasks are sampled with only the corre- sponding subcategory’s traps injected during roll- out, while other settings remain consistent with the original one. Consequently, each subcategory’s +Trap entry in Table 4 reports the model trained and evaluated on the same subcategory. Results on Original Tasks. Despite training each model on a single trap subcategory, all ten runs yield comparable improvements on standard tasks.Averaging across the ten subcategory- specific models, GUI-Owl-7B improves 6.4% and UI-TARS-7B improves 5.9%. This demonstrates that training in adversarial environments yields gen- eral GUI task execution capabilities comparable to those achieved through standard training. Results on Adversarial Tasks.In contrast to the standard environment training, ANTRAP-GRPO yields differentiated robustness gains across trap categories. The models demonstrate substantial improvements against state-level perturbations (S- layer, +8.1%∼11.0%) and most action-level exe- cution errors (A-layer, up to +8.5%, though Intent GUI-Owl-7BUI-TARS-7B Layer SubcategoryBase +Orig +Trap Base +Orig +Trap –Original Task63.1 69.9 † 69.5 † 29.7 36.0 † 35.6 † S Ext. Interruption43.245.354.2 † 28.029.236.4 † Vis. Obscuration57.658.966.5 † 27.128.435.2 † T Tmp. Conflict40.741.945.8 † 21.622.525.8 Vis. Hallucination 58.959.764.0 † 22.923.727.1 A Grounding Err.59.362.767.4 † 17.820.825.4 † Type Mismatch55.558.564.0 † 21.224.228.8 † Int. Deviation57.255.560.621.221.626.7 † R State Deadlock42.442.844.923.724.626.3 Ctx. Disruption26.327.529.213.614.415.7 Loop35.636.036.420.821.221.6 Table 4: Success rates (%) under different subcategories of GRPO training regimes. +Orig: trained in original environment; +Trap: trained in ANTRAP environment. † denotes≥5% absolute improvement over baseline. Deviation gains more modestly at +3.4%∼5.5%). In these cases, successful rollouts against anoma- lies provide clear learning signals for the agents to identify and adapt to specific perturbations, lead- ing to notable robustness improvements. However, gains diminish to a moderate level for reasoning traps (T-layer, +4.2%∼5.1%) and become com- pletely negligible for multi-step contextual anoma- lies (R-layer, under +3.0%, with Loop traps strictly below +1.0%). This indicates that while single- step anomalies are learnable through adversarial exposure, complex contextual traps are difficult to overcome via RL. We attribute this to the agents’ reliance and proficiency in leveraging current-step information rather than integrating broader context for next-step planning. More meta-cognitive, long- horizon self-monitoring capabilities are required for agents to escape circular or deadlocked states. 6 Conclusion We present ANTRAP, a benchmark designed for systematically evaluating GUI agents’ robustness against runtime anomalies in Android environ- ments, featuring a taxonomy STAR and a meticu- lously designed construction pipeline. We evaluate 16 models on ANTRAP, revealing their vulnerabil- ity to dynamic traps across all layers and subcate- gories. We further conduct GRPO training in both original and adversarial environments to delineate the boundary between environment-learnable and reasoning-bottlenecked challenges and find that ad- versarial RL can improve most single-step robust- ness in the state and action layers, while deeper contextual traps remain hard to address. Limitations While our work introduces a fine-grained bench- mark for GUI agents robustness against runtime anomalies, we acknowledge several limitations that open avenues for future research. First, due to the time-consuming nature of annotating and validat- ing the online original task initialization and eval- uation, we have only tested and studied ANTRAP on 236 base tasks. These tasks, while diverse, are still limited and may not fully represent the wide range of common Android GUI operation scenar- ios globally. Future work could apply our method to a broader array of Android scenarios to enhance the generalizability of our findings. Second, due to budget constraints and the difficulty of obtaining annotated trajectory data, we do not explore adver- sarial supervised fine-tuning (SFT) in our study, which has the potential to address issues related to contextual understanding (Wu et al., 2025a; Chen et al., 2025b). 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Every new task is manually verified to confirm that it initializes a valid starting state, can be completed through the standard Android UI, and is correctly judged by its evaluator, so the augmented suite preserves the solvability and technical soundness of the original benchmark. We release all task definitions and evaluators in our anonymous codebase. Framework.ANTRAP augments a standard An- droid environment with an adversarial layer that injects the anomalies defined in our STAR taxon- omy into the agent’s interaction loop, while leaving the underlying tasks, evaluators, and the agent’s rea- soning logic unchanged. The framework consists of three conceptual components: a configuration that specifies which anomaly is instantiated and at which layer, a central controller that orchestrates injection during execution, and four layer-specific modules that realize the ten trap operators of the taxonomy. Because intervention occurs only at the interface between the agent and the environment, instrumenting an existing agent does not require modifying its inference logic. Intervention Points.ANTRAP injects perturba- tions at four points along the agent’s interaction loop, each aligned to one layer of the taxonomy. State layer perturbations modify the observation as it is captured, persistently distorting the input on which all downstream reasoning depends. Think- ing layer perturbations instead act on the observa- tion only at decision time, transiently presenting an old or label-tampered view to the model. Ac- tion layer perturbations are injected between the agent’s output action and its dispatch to the de- vice, offsetting coordinates, remapping the action type, or in the case of State Deadlock, suppress- ing dispatch entirely to simulate an unresponsive device. Round-layer perturbations operate at the trajectory level, supporting traps such as Context Disruption and Loop. When no trap matches at the current step, every intervention reduces to a transparent pass-through, so injection in ANTRAP imposes negligible overhead on clean runs except for the time cost for recovery steps. Trigger Mechanism.In our experiment, at each step, a trap is injected independently with proba- bilityp trap = 0.16, subject to a per-episode cap ofn max = 1, so that every reported episode con- tains exactly one trap event. To preserve enough subsequent steps for the agent to recover, traps are restricted to the first80%of the original step bud- get, and the total budget is extended by a factor of 1.15×. Episodes in which no trap is injected within ten retries are excluded from reporting. In practice, an average of1.3attempts suffices to inject a trap. All stochastic decisions including trap-type sam- pling, inject choice, blur regions, and coordinate offsets are governed by a fixed random seed of 42. History Policy. The ten trap operators fall into two modes, distinguished by whether the perturba- tion enters the agent’s persistent interaction history. Direct modifications perturb the environment as the agent sees it. The altered state is recorded in the log as the state of the current step and remains visible in subsequent reasoning, so we measure whether the agent can recognize and recover from a real change in the world. Induced modifications, in contrast, only record the agent’s misled outputs including offset and remapped actions, without the deceptive observations or the action-hijacking events. This means the agent receives a transiently corrupted observation at decision time, or its output action is silently dropped before execution. Because the agent’s perceptions and stated intent are logged exactly as in a clean run, any resulting failure is self-induced, caused by the agent’s own decisions under momentarily misleading inputs. Thus, the trap measures self-correction rather than tolerance to external perturbation. A.2 Benchmark Reproduction Hardware and Models. Inference runs on a server with Intel Xeon CPUs and four NVIDIA A40 GPUs (48 GB VRAM each). Open- weight VLMs are served by vLLM with tensor- parallel size4, exposed via an OpenAI-compatible API. We use a context length of32,768tokens and at most8images per prompt. Other settings follow their official model-card defaults in huggingface or technical reports. For API-based models (Claude, GPT and Gemini), we use temperature1.0, a4,096- token output cap, and a 8-image history buffer. Evaluation Settings. We run8Android emula- tors in parallel in a Docker container emulating a Pixel 6 on Android API Level 33, with paired con- SubcategoryImplementation Category 1: State Layer (1) External Interruption Pop-ups are triggered through a dedicated TrapOverlay APK installed on the emulator, which regis- ters a SYSTEM_ALERT_WINDOW overlay and reacts to Android Debug Bridge (ADB) broadcasts. Four pop-up types are supported: runtime permission dialogs, modal center dialogs, top notification banners, and fullscreen advertisements. Content is drawn from a curated bank of92realistic Android prompts (21permission,32center-dialog,31banner, and8fullscreen-ad templates). Alterna- tively, the agent’s own Vision-Language Model (VLM) generates content online, conditioned on the foreground-app package name; this mode falls back to the template bank on JSON-parse failure or when the foreground process is a system launcher. (2) Visual Obscuration Pixel-level masks are applied to the captured frame through Pillow. The target region is one of three: a random clickable or editable UI element drawn from the accessibility tree, a uniformly sampled element regardless of affordance, or a random rectangle covering10–30%of the screen. The selected region is replaced by either a Gaussian blur with configurable kernel radius (default r=15) or a solid color patch when a color is specified. Category 2: Thinking Layer (3) Temporal ConflictA rolling buffer of past frames is maintained throughout the episode. At trigger time the current observation passed to the model is replaced with the frame capturedNsteps earlier (defaultN=3), while the unperturbed frame is still saved into the agent’s persistent history. As an alternative, a random screenshot can be sampled from a pre-collected pool of unrelated app states and resized to the current resolution. (4) Visual HallucinationFor each text-bearing UI element identified via its accessibility-tree bounding box, the background color is estimated by median sampling along the bbox edges, the original region is overwritten with that color, and a misleading replacement is rendered in DejaVu Sans with the contrast-inverted color. Replacements are drawn from a curated24-pair antonym dictionary covering common UI verbs such as Save/Delete, Allow/Deny, and Confirm/Cancel; unrecognized labels fall back to a character-level permutation. Category 3: Action Layer (5) Grounding ErrorThe parsed action is intercepted between model output and ADB execution. An independent random offsetδ x ,δ y is added to its coordinates, with magnitude uniformly sampled from[20, 50]pixels and uniformly random sign. The same perturbation is applied to all touch-style actions (click, long_press, double_tap) as well as the start of swipe. The model’s recorded intent is left unchanged so that the offset is only observable through the resulting screenshot. (6) Type MismatchAt the same interception point, the action’s type is rewritten. Touch actions are randomly remapped among the three alternatives click, long_press, and double_tap; swipe is reversed by swapping its start and end coordinates. The model’s response text in the conversation history retains the originally intended action type, so any resulting mismatch must be detected from the post-execution screenshot. (7) Intent DeviationBefore execution, the predicted action is intercepted and changed to a different operation. For touch actions, we change the click coordinates to target a different UI element on the screen, preferring nearby buttons with opposite meanings (e.g., clicking "Cancel" instead of "Confirm"). For type_text actions, the input text is replaced with wrong text from a dictionary or random characters. Importantly, the agent’s history still records its original reasoning and intended action. This creates a mismatch between what the agent planned and what actually happened, forcing the agent to notice the mistake from the next screenshot. Category 4: Round Layer (8) State Deadlock At the execution hook, a block signal is returned forkconsecutive steps, wherekis uniformly sampled from[1,⌊T/4⌋]andTis the original step budget. During this window every ADB command is silently dropped, so the screenshot returned to the agent is identical to the previous one regardless of the action chosen. (9) Context Disruption Immediately before the agent’s step begins, a single ADB keyevent is injected. Two sub- types are supported: KEYCODE_HOME, which returns the device to the launcher, and KEY- CODE_APP_SWITCH, which opens the recent-apps view. The hook then waits one second for the UI to settle so the next screenshot reflects the disrupted context. (10) Loop At a fixed cadence (everyNsteps,N=2by default), KEYCODE_BACK is injected before the agent’s step, forcing repeated backward navigation regardless of the current task progress. The loop terminates once the per-episode trap budget has been spent. Table 5: Detailed implementation of the ten dynamic trap operators in the ANTRAP benchmark. Each row specifies the concrete mechanism, content sources, and parameters used to realize the corresponding trap during evaluation. ComponentHyperparameterValue DataMax Prompt Len.32768 DataMax Response Len.512 DataTrain Batch Size8 Actor/PolicyParallelismFSDP2 Actor/PolicyMicro Batch/GPU1 Actor/PolicyLR1e-6 Actor/PolicyGrad. Clip1.0 Actor/PolicyClip Ratio0.2 SamplingTemp.1.0 SamplingMax Tokens512 SamplingMax Turns50 SamplingMax Pixels1270180 SamplingMin Pixels256 Table 6: Main hyperparameters in GRPO. sole and gRPC ports for network communication. For reproducibility, we fix the task-initialization seed at30and the trap seed at42. Each evaluated episode contains exactly one trap event, with the trigger schedule determined byp trap = 0.16and the retry policy in the previous paragraph. The per-step prompts used for each model are listed in Figures 4 to Figures 9. For the human-level evalua- tion, we recruit five annotators and two authors to independently complete the tasks, and pay $10 per annotator. Special Considerations. Notably, for the GPT models, following the approach of AndroidLab (Xu et al., 2025), we adopt a Set of Marks (SoM) infer- ence style without a grounding requirement. We therefore exclude grounding error traps for GPT models. For instruct models, although they do not follow the ReAct reasoning framework, their of- ficial prompts are designed to guide them to first output a short description of the action before gen- erating the tool call. Therefore, the perturbations injected into the T-layer effectively disrupt the ac- tion description phase for these models. A.3 GRPO Reproduction We conduct full-parameter GRPO on 2 nodes of 8NVIDIA A100 GPUs (80GB), leveraging a cus- tomized VERL framework (Sheng et al., 2025). We construct our training task pool by leveraging orig- inal tasks with randomized parameters. Following a manual verification process to ensure feasibility and strictly exclude overlaps with the test set, we compile a final dataset of 600 training tasks. To optimize throughput, we scale parallel emulators to match the training batch size, accelerate rollouts via vLLM (Kwon et al., 2023), and employ FSDP2 with BF16 mixed precision. The policy has access to the interaction history throughout training. Un- less otherwise specified, these settings apply to all GRPO experiments, with comprehensive hyperpa- rameters detailed in Table 6. BPilot Study for Taxonomy Development Task Collection and Execution. To establish a grounded taxonomy of real-world agent failures, we conduct a large-scale pilot study prior to bench- mark construction. We predefined 600 daily mobile tasks spanning common usage scenarios (e.g., mes- saging, navigation, media playback, settings config- uration, and shopping) across 30+ popular Android applications. We deployed GUI agents including UITARS-1.5-7B, GUI-Owl-7B, and MAIUI-8B on a cloud server and connected to physical Android devices via ADB, executing each task under au- thentic network conditions and system states. All trajectories, including screenshots, model reason- ing, and executed actions at each step, are recorded for subsequent analysis. Annotation Protocol and Taxonomy Derivation. Two of our authors serve as annotators in this stage, independently reviewing the full set of recorded trajectories using a custom web-based annotation interface (Figure 10). Annotators examined every trajectory step-by-step, not only those from failed tasks. This allows us to capture cases where the agent eventually completed the task but encoun- tered intermediate errors requiring recovery. We randomly sampled trajectories from 150 tasks for open coding, where annotators marked the pres- ence of issues at each step and assigned an initial error category with notes. Through iterative dis- cussion and annotation analysis on the remaining 450 tasks, we progressively merged and refined the categories, ultimately forming a four-layer ten- subcategory taxonomy. This ensures that each cat- egory reflects recurring, empirically grounded fail- ure modes rather than hypothetical edge cases. C Qualitative Results of ANTRAP We present qualitative examples of GUI agents’ trajectories under different dynamic traps and dif- ferent settings in the ANTRAP benchmark as shown in Figure 11 to 15. D Mixed-Trap GRPO In the main experiments (Section 5.3), we adopt a per-subcategory training protocol that yields ten GUI-Owl-7B UI-TARS-7B Layer SubcategoryBase +Mixed Base +Mixed –Original Task63.169.129.735.6 S Ext. Interruption43.246.228.030.1 Vis. Obscuration57.659.727.129.2 T Tmp. Conflict40.742.421.622.9 Vis. Hallucination 58.960.222.924.2 A Grounding Err.59.362.317.820.3 Type Mismatch55.558.121.223.7 Int. Deviation57.255.521.221.2 R State Deadlock42.443.223.725.0 Ctx. Disruption26.327.113.614.4 Loop35.636.020.821.2 Table 7: Results of mixed-trap GRPO training, where all ten trap subcategories are randomly injected. specialist models per base agent, each trained on a single trap subcategory. Here we report a sup- plementary experiment using mixed-trap training, where one model is trained per base agent and all ten trap subcategories are randomly sampled during each rollout at an equal probability. The training configuration is otherwise identical with 600 tasks, G = 8rollouts, binary reward. As shown in Ta- ble 7, mixed-trap training yields improvements on original tasks comparable to per-subcategory train- ing. However, the robustness gains across trap sub- categories are substantially diluted, with improve- ments roughly on par with original-environment GRPO (Section 5.2). This indicates that concen- trated, subcategory-specific adversarial exposure is critical for meaningful robustness gains, as random mixing distributes the learning signal too sparsely across diverse recovery strategies, and may need more training samples to converge to a robust pol- icy. Prompt for External Interruption Pop-up Content Generation # === pop-up type 1: Permission dialog === Generate a realistic Android permission request dialog for the app'current_app'. Requirements: - title: 3-6 words, specific to the app (e.g., "Camera Access Required") - message: 10-20 words, explain why the permission is needed - button1: positive action (e.g., "Allow", "Enable") - button2: negative action (e.g., "Deny", "Don't Allow") Output JSON only: "title": "...", "message": "...", "button1": "...", "button2": "..." # === pop-up type 2: Center modal dialog === Generate a realistic Android dialog pop-up that might appear while using'current_app'. Requirements: - title: 3-5 words, dialog purpose (e.g., "Update Available", "Sign In Required") - message: 15-25 words, detailed information about the dialog Output JSON only: "title": "...", "message": "..." # === pop-up type 3: Top notification banner === Generate a realistic Android notification banner that might appear while using'current_app'. Requirements: - title: 2-4 words, notification type (e.g., "New Message", "Update Available") - message: 8-15 words, specific notification content Output JSON only: "title": "...", "message": "..." # === pop-up type 4: Fullscreen advertisement === Generate a realistic full-screen advertisement text for an app that might appear while using 'current_app'. Requirements: - title: 5-10 words, catchy promotional text (e.g., "LIMITED TIME OFFER - 50% OFF TODAY ONLY !") Output JSON only: "title": "..." Figure 4: Prompts used by the agent’s own VLM to generate External Interruption pop-up content, contextualized on the foreground-app package name current_app. The four templates correspond to the four pop-up types rendered by the TrapOverlay APK; on JSON-parse failure or when current_app resolves to a system launcher, the framework falls back to the curated 92-entry template bank described in Section 3.2. System Prompt for Claude-Sonnet You are an AI agent that operates an Android phone by looking at screenshots and choosing one action per turn. Your job is to make steady progress toward the user's goal: never repeat a stuck action, never invent UI elements that aren't visible, and finish with action=terminate ( status="success" or "failure") once the goal is achieved or impossible. # Coordinate system All coordinates are normalized to a 0-999 by 0-999 grid that maps to the entire visible screenshot. (0, 0) is the top-left corner; (999, 999) is the bottom-right. Aim the cursor at the center of the target element, not its edge. # Action vocabulary You may emit exactly one of the following actions per turn: - click — tap a single point. arguments: "action": "click", "coordinate": [x, y] - long_press — press and hold a point for`time` seconds. arguments: "action": "long_press", "coordinate": [x, y], "time": <seconds> - swipe — drag from one point to another (use for scrolling lists, dismissing sheets, opening the app drawer, etc.). arguments: "action": "swipe", "coordinate": [x1, y1], "coordinate2": [x2, y2] - type — type text into the currently focused input box. The text appears as-is; the IME does not interpret tabs or newlines. arguments: "action": "type", "text": "<string>" - key — issue a hardware/keyevent (adb keyevent name, e.g. "volume_up", "power", "clear"). arguments: "action": "key", "text": "<keyevent>" - system_button — press one of the soft system buttons. arguments: "action": "system_button", "button": "Back" | "Home" | "Menu" | "Enter" - open — launch an app by name. The name MUST be from the canonical app list at the bottom of this prompt. arguments: "action": "open", "text": "<app name>" - wait — pause and let the UI settle (use after a navigation that takes a moment to load). arguments: "action": "wait", "time": <seconds> - answer — submit a textual answer to the user's question. Use this for tasks whose goal is information retrieval. arguments: "action": "answer", "text": "<answer>" - terminate — end the task. Always emit this once the goal is fulfilled or judged infeasible. arguments: "action": "terminate", "status": "success" | "failure" # How to think Before acting, briefly reason about: (1) what is on the screen now, (2) whether your previous action achieved its intended effect, and (3) what single concrete step moves the goal forward. Keep reasoning under ~80 words; do not narrate every UI element. If the prior action did not have the expected effect (e.g. a pop-up is still there, the keyboard didn't appear, the app didn't launch), do NOT immediately retry the same action. Diagnose first: dismiss the pop-up, scroll, switch apps, or pick a different target. # Response format Respond every step with EXACTLY two XML blocks, in this order, and nothing else: <reasoning> [Brief reasoning, see "How to think" above.] </reasoning> <tool_call> "name": "mobile_use", "arguments": "action": "<action>", ... </tool_call> The <tool_call> block must contain ONLY a single valid JSON object — no markdown fences, no commentary. # Available apps for action=open Use these exact names (lowercase): Figure 5: System prompt used for Claude-Sonnet in ANTRAP. System Prompt for GPT-5 You are an agent who can operate an Android phone on behalf of a user. Based on user's goal/request, you may - Answer back if the request/goal is a question (or a chat message), like user asks "What is my schedule for today?". - Complete some tasks described in the requests/goals by performing actions (step by step) on the phone. When given a user request, you will try to complete it step by step. At each step, you will be given the current screenshot (including the original screenshot and the same screenshot with bounding boxes and numeric indexes added to some UI elements) and a history of what you have done (in text). Based on these pieces of information and the goal, you must choose to perform one of the action in the following list (action description followed by the JSON format) by outputing the action in the correct JSON format. - If you think the task has been completed, finish the task by using the status action with complete as goal_status:`" action_type": "status", "goal_status": "complete"` - If you think the task is not feasible (including cases like you don't have enough information or can not perform some necessary actions), finish by using the`status` action with infeasible as goal_status:`"action_type": "status", " goal_status": "infeasible"` - Answer user's question:`"action_type": "answer", "text": "<answer_text>"` - Click/tap on an element on the screen. We have added marks (bounding boxes with numeric indexes on their TOP LEFT corner) to most of the UI elements in the screenshot, use the numeric index to indicate which element you want to click: `"action_type": "click", "index": <target_index>`. - Long press on an element on the screen, similar with the click action above, use the numeric label on the bounding box to indicate which element you want to long press:`"action_type": "long_press", "index": <target_index>`. - Type text into a text field (this action contains clicking the text field, typing in the text and pressing the enter, so no need to click on the target field to start), use the numeric label on the bounding box to indicate the target text field:`"action_type": "input_text", "text": <text_input>, "index": <target_index>` - Press the Enter key:`"action_type": "keyboard_enter"` - Navigate to the home screen:`"action_type": "navigate_home"` - Navigate back:`"action_type": "navigate_back"` - Scroll the screen or a scrollable UI element in one of the four directions, use the same numeric index as above if you want to scroll a specific UI element, leave it empty when scroll the whole screen:`"action_type": "scroll", "direction ": <up, down, left, right>, "index": <optional_target_index>` - Open an app (nothing will happen if the app is not installed):`"action_type": "open_app", "app_name": <name>` - Wait for the screen to update:`"action_type": "wait"` Here are some useful guidelines you need to follow: General: - Usually there will be multiple ways to complete a task, pick the easiest one. Also when something does not work as expected (due to various reasons), sometimes a simple retry can solve the problem, but if it doesn't (you can see that from the history), SWITCH to other solutions. - Sometimes you may need to navigate the phone to gather information needed to complete the task, for example if user asks "what is my schedule tomorrow", then you may want to open the calendar app (using the`open_app` action), look up information there, answer user's question (using the`answer` action) and finish (using the`status` action with complete as goal_status). - For requests that are questions (or chat messages), remember to use the`answer` action to reply to user explicitly before finish! Merely displaying the answer on the screen is NOT sufficient (unless the goal is something like "show me ..."). - If the desired state is already achieved (e.g., enabling Wi-Fi when it's already on), you can just complete the task. Action Related: - Use the`open_app` action whenever you want to open an app (nothing will happen if the app is not installed), do not use the app drawer to open an app unless all other ways have failed. - Use the`input_text` action whenever you want to type something (including password) instead of clicking characters on the keyboard one by one. Sometimes there is some default text in the text field you want to type in, remember to delete them before typing. - For`click`,`long_press` and`input_text`, the index parameter you pick must be VISIBLE in the screenshot and also in the UI element list given to you (some elements in the list may NOT be visible on the screen so you can not interact with them). - Consider exploring the screen by using the`scroll` action with different directions to reveal additional content. - The direction parameter for the`scroll` action can be confusing sometimes as it's opposite to swipe, for example, to view content at the bottom, the`scroll` direction should be set to "down". It has been observed that you have difficulties in choosing the correct direction, so if one does not work, try the opposite as well. Text Related Operations: - Normally to select certain text on the screen: <i> Enter text selection mode by long pressing the area where the text is, then some of the words near the long press point will be selected (highlighted with two pointers indicating the range ) and usually a text selection bar will also appear with options like`copy`,`paste`,`select all`, etc. <i> Select the exact text you need. Usually the text selected from the previous step is NOT the one you want, you need to adjust the range by dragging the two pointers. If you want to select all text in the text field, simply click the`select all` button in the bar. - At this point, you don't have the ability to drag something around the screen, so in general you can not select arbitrary text. - To delete some text: the most traditional way is to place the cursor at the right place and use the backspace button in the keyboard to delete the characters one by one (can long press the backspace to accelerate if there are many to delete ). Another approach is to first select the text you want to delete, then click the backspace button in the keyboard. - To copy some text: first select the exact text you want to copy, which usually also brings up the text selection bar, then click the`copy` button in bar. - To paste text into a text box, first long press the text box, then usually the text selection bar will appear with a` paste` button in it. - When typing into a text field, sometimes an auto-complete dropdown list will appear. This usually indicating this is a enum field and you should try to select the best match by clicking the corresponding one in the list. Figure 6: System prompt used for GPT-5 in ANTRAP. Since we use the SOM approach for GPT, we refer to the target element by its index in the screenshot, and the screenshot provided to GPT will have bounding boxes and index annotations for the elements. GPT needs to choose the element to operate on based on this index. System Prompt for MAI-UI You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. ## Output Format For each function call, return the thinking process in <thinking> </thinking> tags, and a json object with function name and arguments within <tool_call></tool_call> XML tags: <thinking> ... </thinking> <tool_call> "name": "mobile_use", "arguments": <args-json-object> </tool_call> ## Action Space "action": "click", "coordinate": [x, y] "action": "long_press", "coordinate": [x, y] "action": "type", "text": "" "action": "swipe", "direction": "up or down or left or right", "coordinate": [x, y] "action": "open", "text": "app_name" "action": "drag", "start_coordinate": [x1, y1], "end_coordinate": [x2, y2] "action": "system_button", "button": "button_name" # Options: back, home, menu, enter "action": "wait" "action": "terminate", "status": "success or fail" "action": "answer", "text": "x" ## Note - Write a small plan and finally summarize your next action (with its target element) in one sentence in <thinking></thinking> part. - Available Apps:`["Camera","Chrome","Clock","Contacts","Dialer","Files","Settings","Markor ","Tasks","Simple Draw Pro","Simple Gallery Pro","Simple SMS Messenger","Audio Recorder","Pro Expense","Broccoli APP","OSMand","VLC","Joplin","Retro Music","OpenTracks","Simple Calendar Pro"]`. You should use the`open` action to open the app as possible as you can, because it is the fast way to open the app. - You must follow the Action Space strictly, and return the correct json object within < thinking> </thinking> and <tool_call></tool_call> XML tags. Figure 7: System prompt used for MAI-UI in ANTRAP. System Prompt for UI-TARS You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. ## Output Format Thought: ... Action: ... ## Action Space click(start_box='[x1, y1, x2, y2]') long_press(start_box='<|box_start|>(x1,y1)<|box_end|>') type(content='x') scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x2,y2)<|box_end|>') open_app(app_name=′) drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x2,y2)<|box_end|>') press_home() press_back() finished(content=′) # Submit the task regardless of whether it succeeds or fails. ## Note - Use English in Thought part. - Write a small plan and finally summarize your next action (with its save target element) in one sentence in Thought part. ## User Instruction instruction Figure 8: System prompt used for UI-TARS in ANTRAP. System Prompt for GUI-Owl-1.5 / Qwen3-VL / Gemini3 # Tools You may call one or more functions to assist with the user query. You are provided with function signatures within <tools></tools> XML tags: <tools> "type": "function", "function": "name_for_human": "mobile_use", "name": "mobile_use", " description": "Use a touchscreen to interact with a mobile device, and take screenshots.\ * This is an interface to a mobile device with touchscreen. You can perform actions like clicking, typing, swiping, etc.\ * Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions.\ * The screen's resolution is 1000x1000.\ * Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked.", "parameters": "properties": "action": "description": "The action to perform. The available actions are:\ *`key`: Perform a key event on the mobile device.\ - This supports adb's`keyevent` syntax.\ - Examples: \\"volume_up\\", \\"volume_down\\", \\" power\\", \\"camera\\", \\"clear\\".\ *`click`: Click the point on the screen with coordinate (x, y).\ *`long_press`: Press the point on the screen with coordinate (x, y) for specified seconds.\ *`swipe`: Swipe from the starting point with coordinate (x, y) to the end point with coordinates2 (x2, y2).\ *`type`: Input the specified text into the activated input box.\ *`answer`: Terminate the current task and output the answer.\ *`system_button `: Press the system button.\ *`open`: Open an app on the device.\ *`wait`: Wait specified seconds for the change to happen.\ *`terminate`: Terminate the current task and report its completion status.", "enum": ["key", "click", "long_press", "swipe", "type", "answer", " system_button", "open", "wait", "terminate"], "type": "string", "coordinate": "description": "(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by`action=click`,`action=long_press`, and`action=swipe`.", "type": "array", "coordinate2": "description": "(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by`action= swipe`.", "type": "array", "text": "description": "Required only by`action=key`,`action= type`,`action=answer`, and`action=open`.", "type": "string", "time": "description": "The seconds to wait. Required only by`action=long_press` and`action=wait`.", "type": "number", "button": "description": "Back means returning to the previous interface, Home means returning to the desktop, Menu means opening the application background menu, and Enter means pressing the enter. Required only by`action=system_button`", "enum": ["Back", "Home", "Menu", "Enter"], "type": "string", "status": "description": "The status of the task. Required only by`action=terminate`.", "type": "string", "enum": ["success", "failure"], "required": [" action"], "type": "object", "args_format": "Format the arguments as a JSON object." </tools> For each function call, return a json object with function name and arguments within < tool_call></tool_call> XML tags: <tool_call> "name": <function-name>, "arguments": <args-json-object> </tool_call> # Response format Respond every step with exactly two parts, in this order: 1)`Action:` followed by one short imperative sentence describing what to do. 2) A single`<tool_call>...</tool_call>` block containing only the JSON: "name": <function- name>, "arguments": <args-json-object>. Rules: - Output Action first, then the <tool_call>. Nothing else outside these two parts. - Coordinates are normalized to the 0-999 grid described in the tool schema. - When using`action=open`, the`text` argument must be one of the canonical AndroidWorld app names listed below. - Finish the task with action=terminate (status="success" or "failure"). Available apps for`action=open` (use these exact names, lowercase): Figure 9: System prompt used for GUI-Owl-1.5 / Qwen3-VL / Gemini3 in ANTRAP. Figure 10: The web interface for preliminary work, through which we use this annotation website to collect performance data of GUI agents conducting trial and error in real-world scenarios, as well as the error cases they encounter. click (546,1748) Step 4 click (978,1664) Step 5 click (546,2231) Step 6 click (401,1725) Step 7 click (978,1664) Step 8 Step 13 ... terminate (success) Figure 11: For the ExpenseDeleteMultiple task in the original set with no trap injection, GUI-Owl-7B successfully completes the task. click (509,1790) Step 4 click (934,1706) Step 5 click (955,1632) Step 6 back () Step 7 click (364,1983) Step 8 Step 14 ... terminate (success) Trap Inject Figure 12: For the ExpenseDeleteMultiple task in the adversarial set with an A-Layer Trap (Grounding Error) injected at step 5, GUI-Owl-7B fails to recover. It proceeds as if the Grounding Error has not occurred, leading to task failure. Step 2 click (420,1536) Step 3 click (546,1748) Step 4 terminate (success) Step 5 Trap Inject swipe(coordinate='(546,1980)' , coordinate2='(537,697)') Figure 13: For the ExpenseDeleteMultiple task in the adversarial set with an R-Layer Trap (Context Disruption) injected at step 5, GUI-Owl-7B fails to recover. It hallucinates task completion, resulting in failure. click (546,1748) Step 4 click (978,1664) Step 5 click (546,2231) Step 6 click (401,1728) Step 7 click (978,1664) Step 8 Step 14 ... terminate (success) Trap Inject Figure 14: For the ExpenseDeleteMultiple task in the adversarial set with an S-Layer Trap (Visual Obscuration) injected at step 5 after GRPO training, GUI-Owl-7B successfully overcomes the perturbation and completes the task. Step 2Step 3Step 4 Step 5 click (456,166) Step 7 Step 8 type ("Pro Expense“) Trap Inject Trap Inject Trap Inject Trap Inject swipe(coordinate='(546,1980)' , coordinate2='(537,697)') swipe(coordinate='(546,1980)' , coordinate2='(537,697)') swipe(coordinate='(546,1982)' , coordinate2='(537,602)') swipe(coordinate='(546,1982)', coordinate2='(537,602)') click (178,406) Step 9 click (546,1748) Step 10 click (978,1664) Step 11 click (546,2231) Step 12 click (401,1725) Step 13 Step 19 ... terminate (success) Figure 15: For the ExpenseDeleteMultiple task in the adversarial set with an R-Layer Trap (State Deadlock) injected at steps 2-5 after GRPO training, GUI-Owl-7B successfully recovers from the disruption and completes the task.