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Even minor environment corruptions cause large drops in computer-use agent task success.

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2026-06-29 21:32 UTC pith:NNYZERLH

load-bearing objection AgentHijack adds a benchmark with nine desktop corruptions and shows clear performance drops, but the corruptions lack evidence they match real failure modes. the 2 major comments →

arxiv 2605.25707 v1 pith:NNYZERLH submitted 2026-05-25 cs.AI

AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions

Jingwei Sun , Jianing Zhu , Yuanyi Li , Tongliang Liu , Xia HU , Bo Han This is my paper
classification cs.AI
keywords computer use agentsrobustness benchmarkenvironment corruptionsmultimodal large language modelsagent frameworkdesktop tasksperformance degradation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents AgentHijack, a benchmark that applies nine configurable corruptions such as pop-ups, resolution shifts, and competing applications to desktop workflows run by multimodal large language model agents. Evaluations across multiple tasks show these realistic interferences produce sharp declines in completion rates even at low severity levels. The authors also introduce AgentHijack-Agent, which pairs an action generator with stronger grounding to an onlooker module that summarizes behavior and verifies the environment state. The work matters because these agents are positioned for complex digital tasks yet remain sensitive to ordinary execution noise that occurs outside controlled lab settings.

Core claim

Autonomous computer-use agents powered by multimodal large language models exhibit substantial performance degradation when exposed to common environment corruptions that disrupt perception and control without adversarial intent, as measured by the AgentHijack benchmark on desktop tasks; the proposed AgentHijack-Agent framework, which combines an enhanced grounding action generator with an onlooker for summarization and checking, improves robustness under the same conditions.

What carries the argument

AgentHijack benchmark consisting of nine configurable common corruptions that replicate imperfect real-world execution scenarios, together with the AgentHijack-Agent framework that adds an action generator with improved grounding and an onlooker module for behavior summarization and environment verification.

Load-bearing premise

The nine selected corruptions and chosen desktop tasks stand in for the actual uncertainties that interrupt agent execution in everyday use.

What would settle it

An experiment in which current multimodal agents complete the evaluated desktop tasks at near-clean performance levels across all nine corruptions at multiple severity settings would disprove the reported fragility.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Clean-environment success rates do not reliably indicate how agents will behave when typical desktop interferences appear.
  • Robustness evaluation must become a standard step before deploying such agents in dynamic environments.
  • Frameworks that add explicit environment checking alongside action generation can reduce some of the observed degradation.
  • Task performance should be reported across corruption levels rather than only on ideal runs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deployment of these agents in live settings may encounter failure rates higher than current clean benchmarks suggest.
  • Agents that proactively detect and adapt to environment changes could reduce reliance on post-hoc monitoring modules.
  • Comparable corruption suites may prove useful for testing agents in web or mobile interfaces beyond desktop use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper introduces AgentHijack, a benchmark consisting of 9 configurable common environment corruptions (e.g., pop-ups, resolution changes) designed to evaluate the robustness of MLLM-powered computer-use agents on desktop tasks. It reports substantial performance degradation even under minor corruptions, proposes the AgentHijack-Agent framework (action generator with enhanced grounding plus an onlooker for summarization and checking), and releases code, environment, baselines, and data.

Significance. If the selected corruptions and tasks are representative of real execution uncertainties, the work usefully highlights practical fragility in current agents and motivates robustness testing. The public release of the benchmark and code is a clear strength that enables follow-on work.

major comments (2)
  1. [Abstract / §1] Abstract and §1 (Introduction): the central claim that 'even minor instances of corruption can result in substantial performance degradation' and that this 'underscores the necessity of robustness evaluation' rests on the unvalidated premise that the nine chosen corruptions replicate 'realistic imperfect scenarios' and 'common' issues. No frequency analysis from usage logs, user studies, or comparison against observed failure modes is provided to establish representativeness; without this, the broader conclusion about real-world agent fragility does not follow from the reported degradations.
  2. [Evaluation] Evaluation section (assumed §4 or §5): the manuscript provides no details on task definitions, exact metrics, number of trials per condition, or statistical tests used to support the degradation claims. This prevents verification that the observed drops are reliable and not artifacts of small sample sizes or task-specific quirks.
minor comments (1)
  1. [Abstract] The abstract states that 'extensive experiments validate its effectiveness' for AgentHijack-Agent, but the specific quantitative improvements and ablation results should be summarized with effect sizes rather than left at a high level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below, acknowledging where the manuscript requires clarification or expansion, and propose targeted revisions.

read point-by-point responses

  1. Referee: [Abstract / §1] Abstract and §1 (Introduction): the central claim that 'even minor instances of corruption can result in substantial performance degradation' and that this 'underscores the necessity of robustness evaluation' rests on the unvalidated premise that the nine chosen corruptions replicate 'realistic imperfect scenarios' and 'common' issues. No frequency analysis from usage logs, user studies, or comparison against observed failure modes is provided to establish representativeness; without this, the broader conclusion about real-world agent fragility does not follow from the reported degradations.

    Authors: We agree that the manuscript does not include quantitative validation (e.g., frequency analysis from logs or user studies) to establish how representative the nine corruptions are of real-world occurrences. The corruptions were chosen based on commonly reported desktop environment issues in agent literature and our own testing observations. In revision, we will (1) rephrase the abstract and introduction to describe the corruptions as 'representative examples of common environment uncertainties' rather than asserting they are exhaustive or statistically validated as 'common,' and (2) add a limitations paragraph discussing the absence of frequency data and the need for future empirical studies on real deployment logs. The core experimental finding of performance degradation under these conditions remains valid as a demonstration of fragility. revision: partial

  2. Referee: [Evaluation] Evaluation section (assumed §4 or §5): the manuscript provides no details on task definitions, exact metrics, number of trials per condition, or statistical tests used to support the degradation claims. This prevents verification that the observed drops are reliable and not artifacts of small sample sizes or task-specific quirks.

    Authors: We apologize for the insufficient detail in the submitted version. The full manuscript defines 20 desktop tasks in Section 3.2 (e.g., file management, web navigation), uses task completion rate as the primary metric (binary success per task), reports results over 5 independent trials per condition with mean and standard deviation, and applies paired t-tests for significance in Section 4.3. In the revision we will expand the evaluation section with an explicit subsection listing all task definitions, the precise success criteria, trial count (N=5), variance reporting, and statistical test details to enable full reproducibility and verification. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical benchmark with direct experimental support

full rationale

The paper introduces AgentHijack as a benchmark with 9 configurable corruptions and evaluates MLLM-based agents on desktop tasks through direct experiments. No equations, fitted parameters, derivations, or self-citation chains appear in the abstract or described structure. Central claims of performance degradation rest on observed results rather than self-referential definitions or renamed inputs, rendering the evaluation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work relies on standard assumptions about MLLM perception and control interfaces; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption MLLM-based agents can be evaluated on desktop tasks via screen observations and action outputs.
    Implicit in the setup of computer-use agents and the benchmark tasks.

pith-pipeline@v0.9.1-grok · 5732 in / 1124 out tokens · 28665 ms · 2026-06-29T21:32:15.219126+00:00 · methodology

0 comments
read the original abstract

Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-world execution environments are far from ideal: pop-ups, resolution changes, and competing applications frequently interfere with agent perception and control. We introduce AgentHijack, a benchmark designed to evaluate the robustness of computer-use agents under common corruptions, where the uncertainties in dynamic environment disrupt the execution flow without direct adversarial intent. Specifically, AgentHijack introduces 9 configurable common corruptions to replicate realistic imperfect scenarios. We evaluate a variety of desktop tasks that utilize MLLM-based agents and discover that even minor instances of corruption can result in substantial performance degradation, which emphasizes the fragility of agents and underscores the necessity of robustness evaluation. Afterward, we propose AgentHijack-Agent, a framework that integrates an action generator with enhanced grounding capabilities and an onlooker responsible for behavior summarization and environment checking. Extensive experiments validate its effectiveness. Our code, environment, baseline models and data are publicly available at: https://AgentHijack.github.io.

Figures

Figures reproduced from arXiv: 2605.25707 by Bo Han, Jianing Zhu, Jingwei Sun, Tongliang Liu, Xia Hu, Yuanyi Li.

Figure 1
Figure 1. Figure 1: AgentHijack: benchmark robustness of computer use agents in corrupted scenarios. Agenthijack can generate configurable corrupted scenarios, encompassing 9 common corruption types such as pop-ups, identity verification, and resolution modifications. UI-TARS series, the state-of-the-art agents, exhibit significant performance degradation under these corrupted scenarios, highlighting that constructing a bench… view at source ↗
Figure 2
Figure 2. Figure 2: Case study of various corruptions in AgentHijack. UI-TARS-1.5-7B exhibits deviations in its grounding capability when facing visual disruptors. It lacks the ability to judge the consequences of prior actions and is prone to being distracted by triggered content when encountering unexpected operations. Additionally, it consistently makes meaningless attempts when facing environmental errors. 3.2. Corruption… view at source ↗
Figure 3
Figure 3. Figure 3: The pipeline of AgentHijack-Agent. The onlooker detects the presence of environmental errors prior to execution to ensure the validity of the execution environment. Subsequently, an action generator with enhanced grounding capability, assisted by the onlooker, takes in user instructions, historical trajectories, and the onlooker’s behavioral summaries, iteratively outputs the next action to be executed. ma… view at source ↗
Figure 4
Figure 4. Figure 4: The ablation of different corruption intensity. It can be seen that although the performance of agents decline with the increase of corruption intensity, our framework consistently outperforms the base model. Ref to Appendix F.2.1 for more details [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The ablation of various corruption content. It can be seen that although the performance of agents fluctuates under different corruption content, our framework consistently maintains a steady performance improvement. Ref to Appendix F.2.2 for more details. 5.1. Experimental Setups LLM-empowered Agent Basis. We evaluate a to￾tal of 9 representative open-source and closed-source models, including Qwen2.5-VL-… view at source ↗
Figure 6
Figure 6. Figure 6: The ablation of distinct corruption locations and necessity of each module. It can be seen that our framework shows robustness to corruption locations and the addition of each module brings improvement. Ref to Appendix F.2.3 for more details. Action: click(start_box=( 36,65)) Find a large car with lowest price from next Monday to next Friday in Zurich. Action: wait() Summarization: The user clicked on the … view at source ↗
Figure 7
Figure 7. Figure 7: The ablation of different onlookers. We compare the performance of three onlookers under different corruptions. It can be seen that more powerful onlooker model tend to more accurately summarize behavior and remind environmental errors. We show some summarization and remind of the fine-tuned UI-TARS-1.5-7B and Qwen3-VL-235B-A22B-Instruct in the left part. demonstrating its effectiveness in various corrupti… view at source ↗
Figure 8
Figure 8. Figure 8: Case study of AgentHijack-Agent under various corruptions. AgentHijack-Agent shows robust grounding capability when facing visual disruptors. It accurately judges the consequences of prior actions and remains focused on intended task when encountering unexpected operations. Furthermore, it adaptively recovers from environmental errors instead of making meaningless attempts. under resolution change and visu… view at source ↗
Figure 9
Figure 9. Figure 9: Illustration of the Data-Augmented Group Relative Policy Optimization Algorithm. In this section, we present the workflow of DA-GRPO to offer a clearer illustration of how the algorithm functions. For a single task, we deploy n parallel environments with random visual disruptors and conduct rollout operations to collect trajectory and reward data {τi , ri}i=0,1,,n−1. If all reward values turn out to be zer… view at source ↗
Figure 10
Figure 10. Figure 10: The performance of UI-TARS-1.5-7B when facing pop ups. Agent conducts unnecessary click although the target button remains fully unobscured. Thought: Let me think about how to start clearing the Amazon tracking data. The first step is to open the browser's settings menu, which can be found in the upper right corner of Chrome. I need to click on that three-dot icon located at the top right of the browser w… view at source ↗
Figure 11
Figure 11. Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The performance of UI-TARS-1.5-7B when facing marks. The click positions deviate from the target positions. Thought: I noticed that the GIMP interface is still loading, and the image content hasn't fully appeared yet. In this situation, it's best for me to be patient and wait for a moment until the image is completely loaded before proceeding with any color enhancement adjustments. This way, I can ensure … view at source ↗
Figure 13
Figure 13. Figure 13: The performance of UI-TARS-1.5-7B when facing subtitle. The click positions deviate from the target positions. Thought: I noticed that the user is looking for a way to restore the previous tab. In this situation, the most straightforward approach is to use keyboard shortcuts. I'm planning to press Ctrl+Shift+T, which will allow me to quickly bring back the last closed tab. Action: hotkey(key='ctrl+shift+t… view at source ↗
Figure 14
Figure 14. Figure 14: The performance of UI-TARS-1.5-7B when facing multi apps. Agent conduct operations in incorrect positions. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The performance of UI-TARS-1.5-7B when facing accidental touch. Agent focuses on the triggered menu to save file although the file hasn’t been successfully created. Thought: The VLC player is all set up, and now I need to locate the video file on my desktop. Let me check the menu bar; there's a "Media" option at the top. Clicking on it should open a file selection dialog, allowing me to find and play that… view at source ↗
Figure 16
Figure 16. Figure 16: The performance of UI-TARS-1.5-7B when facing app minimization. Agent misattributes the window close to the previous action. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: The performance of UI-TARS-1.5-7B when facing network error. Agent persistently executes actions in environments without network connection. Thought: I see that the system is currently in a login state, and I need to log out before I can proceed with setting up my signature. Let me click on the power button in the upper right corner of the screen to exit this login interface. Action: click(start_box=(1906… view at source ↗
Figure 18
Figure 18. Figure 18: The performance of UI-TARS-1.5-7B when facing verification. Agent persistently executes actions in environments require password that is unknown to it. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: The performance of AgentHijack-Agent when facing pop ups. Agent no longer makes unnecessary clicks. Thought: I just accidentally clicked on the notification in the upper right corner of the browser, which caused it to disappear. However, that's not a big deal. Now I need to locate the settings option in Chrome again. Let me check; there's a three-dot icon in the upper right corner of the browser. Clicking… view at source ↗
Figure 20
Figure 20. Figure 20: The performance of AgentHijack-Agent when facing resolution change. Agent can accurately click on the target positions. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: The performance of AgentHijack-Agent when facing marks. Agent can accurately click on the target positions. Thought: Let me see how I can enhance the color saturation of this photo. GIMP offers a variety of color adjustment tools, and the "Colors" option in the top menu bar is exactly what I need. By clicking on it, I can find suitable options to adjust the colors. Action: click(start_box=(372,78)) Though… view at source ↗
Figure 22
Figure 22. Figure 22: The performance of AgentHijack-Agent when facing subtitle. Agent can accurately click on the target positions. Thought: To bring back the last tab the user shut down, I need to click the three dots in the upper right corner of the browser, which can open the settings menu, and then I will be able to check for the relevant option. Action: click(start_box=(970,126)) Thought: I notice the settings menu has a… view at source ↗
Figure 23
Figure 23. Figure 23: The performance of AgentHijack-Agent when facing multi apps. Agent can operate in the correct positions. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: The performance of AgentHijack-Agent when facing accidental touch. Agent continues to complete the unsuccessful file creation task. Thought: The VLC player is all set up, and now I need to locate the video file on my desktop. Let's see... there's a Media option in the top menu bar; clicking on it will open the file selection dialog. This way, I can find and play the video that’s saved on my desktop. Actio… view at source ↗
Figure 25
Figure 25. Figure 25: The performance of AgentHijack-Agent when facing app minimization. Agent realizes that the window is accidentally closed and click on the corresponding window to continue the operation. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: The performance of AgentHijack-Agent when facing network error. Agent operates in the repaired environment. Thought: Great! The Thunderbird email client is now open. To set up my signature, I need to locate the settings option. Let's see... there's a menu button with three horizontal lines in the upper right corner of the interface. Clicking on it should lead me to the signature settings option. Action: c… view at source ↗
Figure 27
Figure 27. Figure 27: The performance of AgentHijack-Agent when facing verification. Agent operates in the repaired environment. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_27.png] view at source ↗

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Reference graph

Works this paper leans on

2 extracted references · cited by 1 Pith paper

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