REVIEW 2 major objections 2 minor 192 references
Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
T0 review · 2 major / 2 minor · reviewed 2026-05-11 · grok-4.3
Pith's one-line read A unified architecture-lifecycle framework secures computer-use agents by grounding reliability in deployment realities.
desk verdict The paper gives a clean organizational framework for CUA security by splitting architecture into Perception-Decision-Execution and lifecycle into Creation-Deployment-Operation-Maintenance, but offers only re-categorization of prior work with no new tests or proofs. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The architecture-lifecycle framework that couples three layers (Perception, Decision, Execution) transforming observations into actions with four stages (Creation, Deployment, Operation, Maintenance) where conditions are set and assurance maintained.
What would settle it
Applying the framework to a collection of current computer-use agent systems and finding that it fails to reveal any additional intervention surfaces or control points beyond what prior surveys already noted would show the framework adds no new distinguishing power.
Extended reading notes
Core claim
The article develops an architecture-lifecycle framework for deployment-grounded reliability in computer-use agents. The architectural view analyzes Perception, Decision, and Execution as coupled layers that transform software observations into authority-bearing actions. The lifecycle view examines Creation, Deployment, Operation, and Maintenance as stages in which priors are learned, tools and permissions are bound, runtime trajectories are stressed, and assurance must be preserved under drift. Using this lens, the analysis synthesizes representative systems, benchmarks, and security studies to distinguish where failures become visible from where enabling conditions are introduced and to 1.
Load-bearing premise
That reviewing and synthesizing existing systems, benchmarks, and studies through these specific layers and stages will clearly separate visible failures from their enabling conditions without needing original experiments to validate the framework.
Editorial extensions
If this is right
- Control oversight can target specific intervention surfaces identified across layers and stages.
- Assurance preservation under drift becomes possible through maintenance stage analysis.
- Open challenges like controllable grounding and safe authority binding are highlighted for future work.
- Privacy-preserving memory and mixed-trust runtime defense emerge as key needs.
Reading between the lines
- Developers could use the framework to audit new computer-use agents for hidden permission risks before deployment.
- The approach might extend to creating benchmarks that evaluate agents across full lifecycles rather than isolated tasks.
- Similar layered views could apply to other autonomous systems beyond software agents, such as robotic controllers.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims to develop a unified architecture-lifecycle framework for deployment-grounded reliability in computer-use agents (CUAs). The architectural view decomposes agents into coupled Perception, Decision, and Execution layers that convert observations into authority-bearing actions. The lifecycle view organizes the process into Creation, Deployment, Operation, and Maintenance stages where priors are acquired, tools/permissions bound, trajectories stressed, and assurance maintained under drift. Applying this lens to existing systems, benchmarks, and security studies, the work distinguishes locations where failures become visible from where enabling conditions are introduced and identifies recurring intervention surfaces for oversight. OpenClaw serves only as a public motivating example of an open deployment pattern.
Significance. If the framework provides a coherent organizational lens that reveals non-obvious mappings between architectural layers, lifecycle stages, and control surfaces, it could aid researchers and practitioners in systematically addressing reliability and security gaps in real-world CUA deployments beyond task-success metrics. The explicit connection of capability formation to authority exposure and failure manifestation is a potentially useful synthesis, though its value hinges on whether the distinctions prove actionable in subsequent empirical work.
major comments (2)
- [Abstract / synthesis of representative systems] Abstract and synthesis description: The central claim that the architecture-lifecycle lens 'distinguishes where failures become visible from where their enabling conditions are introduced' and thereby 'maps recurring intervention surfaces' rests entirely on qualitative re-categorization of prior literature. No formal categorization protocol, inter-annotator agreement, or controlled comparison against existing survey taxonomies is provided to establish that the distinctions are novel or practically enabling.
- [Lifecycle view] Lifecycle view description: The mapping of stages (Creation through Maintenance) to specific reliability mechanisms (prior learning, permission binding, drift handling) is asserted conceptually, but the manuscript supplies no concrete, traceable examples from the cited benchmarks or security studies demonstrating how this mapping uncovers intervention surfaces that prior analyses missed.
minor comments (2)
- [Abstract] The abstract contains multiple typographical and formatting issues, including 'flesystems' (should be 'filesystems'), missing spaces after commas, 'Creation.Deployment' (period instead of comma), and hyphenated 'author-ity'.
- [Introduction / motivating example] The precise scope and depth of the OpenClaw example should be stated explicitly in the main text to avoid any implication that it constitutes an internal case study.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed feedback. We address each major comment below, providing clarifications on the scope of our qualitative framework and committing to revisions that strengthen the presentation of mappings and examples.
read point-by-point responses
-
Referee: [Abstract / synthesis of representative systems] Abstract and synthesis description: The central claim that the architecture-lifecycle lens 'distinguishes where failures become visible from where their enabling conditions are introduced' and thereby 'maps recurring intervention surfaces' rests entirely on qualitative re-categorization of prior literature. No formal categorization protocol, inter-annotator agreement, or controlled comparison against existing survey taxonomies is provided to establish that the distinctions are novel or practically enabling.
Authors: We agree that the synthesis is qualitative and does not employ a formal categorization protocol, inter-annotator agreement, or controlled comparison to existing taxonomies. The framework is offered as a conceptual organizational lens to connect architectural layers, lifecycle stages, and control surfaces, rather than as an empirically derived taxonomy. The distinctions emerge from re-examining cited systems, benchmarks, and security studies through this coupled structure, which prior surveys have not organized in this way. We will revise the abstract, introduction, and synthesis section to explicitly characterize the approach as qualitative and include a summary table of layer-stage-intervention mappings to improve transparency and actionability. revision: partial
-
Referee: [Lifecycle view] Lifecycle view description: The mapping of stages (Creation through Maintenance) to specific reliability mechanisms (prior learning, permission binding, drift handling) is asserted conceptually, but the manuscript supplies no concrete, traceable examples from the cited benchmarks or security studies demonstrating how this mapping uncovers intervention surfaces that prior analyses missed.
Authors: The referee is correct that while the manuscript applies the lens to representative works, more explicit tracing of individual examples would better demonstrate novel distinctions. The synthesis section currently references studies on perception errors, tool mediation, and runtime oversight, but does not always isolate how a specific enabling condition in one stage manifests in another. In revision we will add a new subsection with 2-3 traceable examples (e.g., linking a cited agent security study on permission overreach in Deployment to observable failures in Operation) to show intervention surfaces that were not foregrounded in the original analyses. revision: yes
Circularity Check
No circularity: conceptual framework is a non-derivational synthesis
full rationale
The manuscript proposes an architecture-lifecycle framework consisting of Perception-Decision-Execution layers and Creation-Deployment-Operation-Maintenance stages. It applies this lens to synthesize representative systems, benchmarks, and studies from prior literature, distinguishing failure visibility from enabling conditions via author-led categorization. No equations, derivations, fitted parameters, predictions, uniqueness theorems, or self-citations appear in the text. OpenClaw is explicitly limited to a public motivating example. The central claim is an interpretive organizational lens rather than a reduction of any result to its inputs by construction, rendering the derivation chain self-contained with no load-bearing circular steps.
Assumptions & free parameters
assumptions (2)
- domain assumption Perception, Decision, and Execution function as coupled layers that transform software observations into authority-bearing actions.
- domain assumption Reliability and security issues arise and can be addressed across Creation, Deployment, Operation, and Maintenance stages.
Cite this review
Pith. "Pith review of Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability." pith.science (2026). https://pith.science/paper/2605.07110
@misc{pith2026260507110,
author = {Pith},
title = {Pith review of: Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability},
year = {2026},
howpublished = {\url{https://pith.science/paper/2605.07110}},
note = {Machine review of arXiv:2605.07110}
}
read the original abstract
Computer-use agents(CUAs)are moving frombounded benchmarks toward real software environments, wherethey operate browsers, desktops, mobile applications, flesystems,terminals, and tool backends. In such settings, reliability isno longer captured by task success alone: perception errors,planning drift, memory use, tool mediation, permission scope,and runtime oversight jointly determine whether agent actionsremain aligned with user intent, Existing surveys organize theCUA landscape by methods, platforms, benchmarks, or securitythreats, but less explicitly connect capability formation, author-ity exposure, failure manifestation, and control placement. Toaddress this gap, the article develops an architecture-lifecycleframework for deployment-grounded reliability in CUAs. Thearchitectural view analyzes Perception, Decision, and Executionas coupled layers that transform software observations intoauthority-bearing actions, The lifecycle view examines Creation.Deployment, Operation, and Maintenance as stages in which priorsare learned, tools and permissions are bound, runtime trajecto.ries are stressed, and assurance must be preserved under drift.Using this lens, the analysis synthesizes representative systems,benchmarks, and security/privacy studies; distinguishes wherefailures become visible from where their enabling conditions areintroduced, and maps recurring intervention surfaces for controloversight, and assurance. OpenClaw is used only as a public moti.vating example of an open deployment pattern, not as a verifedinternal case study. The conclusion highlights open challengesin controllable grounding, long-horizon constraint preservation,safe authority binding, mixed-trust runtime defense, privacy-preserving memory,and continual assurance.
Figures
Reference graph
Works this paper leans on
-
[1]
OpenCUA: Open foundations for computer-use agents.arXiv preprint arXiv:2508.09123,
Xinyuan Wang, Bowen Wang, Dunjie Lu, Junlin Yang, Tianbao Xie, Junli Wang, Jiaqi Deng, Xiaole Guo, Yiheng Xu, Chen Henry Wu, et al. Opencua: Open foundations for computer-use agents.arXiv preprint arXiv:2508.09123, 2025
-
[2]
arXiv preprint arXiv:2510.02250 , year=
Gonzalo Gonzalez-Pumariega, Vincent Tu, Chih-Lun Lee, Jiachen Yang, Ang Li, and Xin Eric Wang. The unreasonable effectiveness of scaling agents for computer use.arXiv preprint arXiv:2510.02250, 2025
-
[3]
arXiv preprint arXiv:2602.16855 , year=
Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu, Shengjie Zhou, Xuhao Hu, Feiyu Gao, Junjie Cao, Zihua Wang, et al. Mobile-agent-v3. 5: Multi-platform fundamental gui agents.arXiv preprint arXiv:2602.16855, 2026
-
[4]
arXiv preprint arXiv:2512.22047 , year=
Hanzhang Zhou, Xu Zhang, Panrong Tong, Jianan Zhang, Liangyu Chen, Quyu Kong, Chenglin Cai, Chen Liu, Yue Wang, Jingren Zhou, et al. Mai-ui technical report: Real-world centric foundation gui agents. arXiv preprint arXiv:2512.22047, 2025
-
[5]
arXiv preprint arXiv:2512.15431 , year=
Haolong Yan, Jia Wang, Xin Huang, Yeqing Shen, Ziyang Meng, Zhimin Fan, Kaijun Tan, Jin Gao, Lieyu Shi, Mi Yang, et al. Step-gui technical report.arXiv preprint arXiv:2512.15431, 2025
-
[6]
arXiv2510.19949(2025).https : / / doi
Mathieu Andreux, M ¨art Bakler, Yanael Barbier, Hamza Benchekroun, Emilien Bir ´e, Antoine Bonnet, Riaz Bordie, Nathan Bout, Matthias Brunel, Aleix Cambray, et al. Surfer 2: The next generation of cross- platform computer use agents.arXiv preprint arXiv:2510.19949, 2025
-
[7]
MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Tanmay Gupta, Piper Wolters, Zixian Ma, Peter Sushko, Rock Yuren Pang, Diego Llanes, Yue Yang, Taira Anderson, Boyuan Zheng, Zhongzheng Ren, et al. Molmoweb: Open visual web agent and open data for the open web.arXiv preprint arXiv:2604.08516, 2026
work page Pith review arXiv 2026
-
[8]
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114, 2023
work page 2023
Show all 192 references
-
[9]
Webarena: A realistic web environment for building autonomous agents.arXiv preprint arXiv:2307.13854, 2023
Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, et al. Webarena: A realistic web environment for building autonomous agents.arXiv preprint arXiv:2307.13854, 2023
2023 arXiv
-
[10]
Visualwebarena: Evaluating multimodal agents on realistic visual web tasks
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, and Daniel Fried. Visualwebarena: Evaluating multimodal agents on realistic visual web tasks. InProceedings of the 62nd Annual Meeting of the Associatio...
2024
-
[11]
Workarena: How capable are web agents at solving common knowledge work tasks?arXiv preprint arXiv:2403.07718, 2024
Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H Laradji, Manuel Del Verme, Tom Marty, L´eo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, et al. Workarena: How capable are web agents at solving common knowledge work tasks?arXiv preprint arXiv:2403.07718, 2024
2024
-
[12]
Workarena++: Towards composi- tional planning and reasoning-based common knowledge work tasks
L ´eo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault L De Chezelles, Quentin Cappart, Nicolas Chapados, Alexan- dre Lacoste, and Alexandre Drouin. Workarena++: Towards composi- tional planning and reasoning-based common knowledge work tasks. Advances in Neural ...
2024
-
[13]
Webforge: Breaking the realism-reproducibility-scalability trilemma in browser agent benchmark.arXiv preprint arXiv:2604.10988, 2026
Peng Yuan, Yuyang Yin, Yuxuan Cai, and Zheng Wei. Webforge: Breaking the realism-reproducibility-scalability trilemma in browser agent benchmark.arXiv preprint arXiv:2604.10988, 2026
2026 arXiv
-
[14]
Clawbench: Can ai agents complete everyday online tasks? arXiv preprint arXiv:2604.08523, 2026
Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, Junwen Miao, Xuan Lu, Wendong Xu, Yunzhuo Hao, Songcheng Cai, Xiaochen Wang, et al. Clawbench: Can ai agents complete everyday online tasks? arXiv preprint arXiv:2604.08523, 2026
2026 arXiv
-
[15]
Knowu-bench: Towards interactive, proactive, and personalized mobile agent evaluation.arXiv preprint arXiv:2604.08455, 2026
Tongbo Chen, Zhengxi Lu, Zhan Xu, Guocheng Shao, Shaohan Zhao, Fei Tang, Yong Du, Kaitao Song, Yizhou Liu, Yuchen Yan, et al. Knowu-bench: Towards interactive, proactive, and personalized mobile agent evaluation.arXiv preprint arXiv:2604.08455, 2026
2026 arXiv
-
[16]
Pspa-bench: A personalized benchmark for smartphone gui agent.arXiv preprint arXiv:2603.29318, 2026
Hongyi Nie, Xunyuan Liu, Yudong Bai, Yaqing Wang, Yang Liu, Quan- ming Yao, and Zhen Wang. Pspa-bench: A personalized benchmark for smartphone gui agent.arXiv preprint arXiv:2603.29318, 2026
2026
-
[17]
Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024
Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Sy...
2024
-
[18]
Colorbrowseragent: Complex long-horizon browser agent with adaptive knowledge evolution
Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, HUARONG DENG, and Jun Wang. Colorbrowseragent: Complex long-horizon browser agent with adaptive knowledge evolution. InThe 64th Annual Meeting of the Association for Compu...
2026
-
[19]
GraphPilot: GUI Task Automa- tion with One-Step LLM Reasoning Powered by Knowledge Graph, jan 2026
Mingxian Yu, Siqi Luo, and Xu Chen. GraphPilot: GUI Task Automa- tion with One-Step LLM Reasoning Powered by Knowledge Graph, jan 2026. Journal of Intelligent Computing and Networking
2026
-
[20]
Showui-aloha: Human-taught gui agent.arXiv preprint arXiv:2601.07181, 2026
Yichun Zhang, Xiangwu Guo, Yauhong Goh, Jessica Hu, Zhiheng Chen, Xin Wang, Difei Gao, and Mike Zheng Shou. Showui-aloha: Human-taught gui agent.arXiv preprint arXiv:2601.07181, 2026
2026
-
[21]
Gui agents: A survey
Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, et al. Gui agents: A survey. InFindings of the Association for Computational Linguistics: ACL 2025, pages 22522–22538, 2025
2025
-
[22]
Os agents: A survey on mllm-based agents for computer, phone and browser use
Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, et al. Os agents: A survey on mllm-based agents for computer, phone and browser use. InProceedings of the 63rd Annual Meeting of the Association for Computati...
2025
-
[23]
A survey on (m) llm-based gui agents.arXiv preprint arXiv:2504.13865, 2025
Fei Tang, Haolei Xu, Hang Zhang, Siqi Chen, Xingyu Wu, Yongliang Shen, Wenqi Zhang, Guiyang Hou, Zeqi Tan, Yuchen Yan, et al. A survey on (m) llm-based gui agents.arXiv preprint arXiv:2504.13865, 2025
2025
-
[24]
Gui agents with foundation models: A comprehensive survey.arXiv preprint arXiv:2411.04890, 2024
Shuai Wang, Weiwen Liu, Jingxuan Chen, Yuqi Zhou, Weinan Gan, Xingshan Zeng, Yuhan Che, Shuai Yu, Xinlong Hao, Kun Shao, et al. Gui agents with foundation models: A comprehensive survey.arXiv preprint arXiv:2411.04890, 2024
2024
-
[25]
Generalist virtual agents: A survey on autonomous agents across digital platforms.arXiv preprint arXiv:2411.10943, 2024
Minghe Gao, Wendong Bu, Bingchen Miao, Yang Wu, Yunfei Li, Juncheng Li, Siliang Tang, Qi Wu, Yueting Zhuang, and Meng Wang. Generalist virtual agents: A survey on autonomous agents across digital platforms.arXiv preprint arXiv:2411.10943, 2024
2024
-
[26]
Towards trustworthy gui agents: A survey.arXiv preprint arXiv:2503.23434, 2025
Yucheng Shi, Wenhao Yu, Wenlin Yao, Wenhu Chen, and Ninghao Liu. Towards trustworthy gui agents: A survey.arXiv preprint arXiv:2503.23434, 2025
2025
-
[27]
A survey on gui agents with foun- dation models enhanced by reinforcement learning.arXiv preprint arXiv:2504.20464, 2025
Jiahao Li and Kaer Huang. A survey on gui agents with foun- dation models enhanced by reinforcement learning.arXiv preprint arXiv:2504.20464, 2025
2025
-
[28]
Llm-powered gui agents in phone automation: Surveying progress and prospects.arXiv preprint arXiv:2504.19838, 2025
Guangyi Liu, Pengxiang Zhao, Yaozhen Liang, Liang Liu, Yaxuan Guo, Han Xiao, Weifeng Lin, Yuxiang Chai, Yue Han, Shuai Ren, et al. Llm-powered gui agents in phone automation: Surveying progress and prospects.arXiv preprint arXiv:2504.19838, 2025
2025
-
[29]
A survey of webagents: Towards next-generation ai agents for web automation with large foundation models
Liangbo Ning, Ziran Liang, Zhuohang Jiang, Haohao Qu, Yujuan Ding, Wenqi Fan, Xiao-yong Wei, Shanru Lin, Hui Liu, Philip S Yu, et al. A survey of webagents: Towards next-generation ai agents for web automation with large foundation models. In31st ACM SIGKDD Conference on Knowl...
2025
-
[30]
A survey on the safety and security threats of computer-using agents: Jarvis or ultron?arXiv preprint arXiv:2505.10924, 2025
Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen- tse Huang, Kun Wang, Wenxuan Wang, and Shuai Wang. A survey on the safety and security threats of computer-using agents: Jarvis or ultron?arXiv preprint arXiv:2505.10924, 2025
2025 arXiv
-
[31]
Openclaw — personal ai assistant
OpenClaw. Openclaw — personal ai assistant. https://openclaw.ai/,
-
[32]
Website, accessed: 2026-04-11
2026
-
[33]
Openclaw, moltbook and the future of ai agents
Aili McConnon. Openclaw, moltbook and the future of ai agents. https://www.ibm.com/think/news/clawdbot-ai-agent-testing-limits-ver tical-integration, 2026. IBM Think article, accessed: 2026-04-11
2026
-
[34]
Clawgui: A unified framework for training, evaluating, and deploying gui agents.arXiv preprint arXiv:2604.11784, 2026
Fei Tang, Zhiqiong Lu, Boxuan Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. Clawgui: A unified framework for training, evaluating, and deploying gui agents.arXiv preprint arXiv:2604.11784, 2026
2026 arXiv
-
[35]
LongHorizonUI: A Unified Framework for Robust long-horizon Task Automation of GUI Agent
Bin Kang, Shaoguo Wen, Yifei Bi, Shunlong Wu, Xinbin Yuan, Rui Shao, Junle Wang, and Zhuotao Tian. LongHorizonUI: A Unified Framework for Robust long-horizon Task Automation of GUI Agent. InInternational Conference on Learning Representations, 2026. Poster
2026
-
[36]
Webarena-infinity: Generating browser environments with verifiable tasks at scale.shuyanzhou.com, March 2026
Shuyan Zhou. Webarena-infinity: Generating browser environments with verifiable tasks at scale.shuyanzhou.com, March 2026
2026
-
[37]
Webpii: Benchmarking visual pii detection for computer- use agents.arXiv preprint arXiv:2603.17357, 2026
Nathan Zhao. Webpii: Benchmarking visual pii detection for computer- use agents.arXiv preprint arXiv:2603.17357, 2026
2026
-
[38]
Guide: Interpretable gui agent evaluation via hierarchical diagnosis.arXiv preprint arXiv:2604.04399, 2026
Yuwen Zhai, Runze Li, Liang Wang, Nian Shi, Liwu Xu, Wei Zhang, Ran Lin, Bo Xu, and Benlei Cui. Guide: Interpretable gui agent evaluation via hierarchical diagnosis.arXiv preprint arXiv:2604.04399, 2026
2026 arXiv
-
[39]
Cuarewardbench: A benchmark for evaluating reward models on computer-using agent
Haojia Lin, Xiaoyu Tan, Yulei Qin, Zihan Xu, Yuchen Shi, Zongyi Li, Gang Li, Shaofei Cai, Siqi Cai, Chaoyou Fu, et al. Cuarewardbench: A benchmark for evaluating reward models on computer-using agent. arXiv preprint arXiv:2510.18596, 2025
2025
-
[40]
Gym-anything: Turn any software into an agent environment.arXiv preprint arXiv:2604.06126, 2026
Pranjal Aggarwal, Graham Neubig, and Sean Welleck. Gym-anything: Turn any software into an agent environment.arXiv preprint arXiv:2604.06126, 2026
2026 arXiv
-
[41]
Os-harm: A benchmark for measuring safety of computer use agents.arXiv preprint arXiv:2506.14866, 2025
Thomas Kuntz, Agatha Duzan, Hao Zhao, Francesco Croce, Zico Kolter, Nicolas Flammarion, and Maksym Andriushchenko. Os-harm: A benchmark for measuring safety of computer use agents.arXiv preprint arXiv:2506.14866, 2025
2025
-
[42]
Wainjectbench: Benchmarking prompt injection detections for web agents.arXiv preprint arXiv:2510.01354, 2025
Yinuo Liu, Ruohan Xu, Xilong Wang, Yuqi Jia, and Neil Zhenqiang Gong. Wainjectbench: Benchmarking prompt injection detections for web agents.arXiv preprint arXiv:2510.01354, 2025
2025
-
[43]
Risky-bench: Probing agentic safety risks under real-world deployment.arXiv preprint arXiv:2602.03100, 2026
Jingnan Zheng, Yanzhen Luo, Jingjun Xu, Bingnan Liu, Yuxin Chen, Chenhang Cui, Gelei Deng, Chaochao Lu, Xiang Wang, An Zhang, et al. Risky-bench: Probing agentic safety risks under real-world deployment.arXiv preprint arXiv:2602.03100, 2026
2026
-
[44]
The aegis protocol: A foundational security framework for autonomous ai agents
Sai Teja Reddy Adapala and Yashwanth Reddy Alugubelly. The aegis protocol: A foundational security framework for autonomous ai agents. arXiv preprint arXiv:2508.19267, 2025
2025
-
[45]
A survey of agentic ai and cybersecurity: Challenges, opportunities and use-case prototypes.arXiv preprint arXiv:2601.05293, 2026
Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta, and Elisa Bertino. A survey of agentic ai and cybersecurity: Challenges, opportunities and use-case prototypes.arXiv preprint arXiv:2601.05293, 2026
2026
-
[46]
Os-copilot: Towards generalist computer agents with self-improvement.arXiv preprint arXiv:2402.07456, 2024
Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumi- anze Liu, Shunyu Yao, Tao Yu, and Lingpeng Kong. Os-copilot: Towards generalist computer agents with self-improvement.arXiv preprint arXiv:2402.07456, 2024
2024
-
[47]
Coact-1: Computer-using agents with coding as actions.arXiv preprint arXiv:2508.03923, 2025
Linxin Song, Yutong Dai, Viraj Prabhu, Jieyu Zhang, Taiwei Shi, Li Li, Junnan Li, Silvio Savarese, Zeyuan Chen, Jieyu Zhao, et al. Coact-1: Computer-using agents with coding as actions.arXiv preprint arXiv:2508.03923, 2025
2025
-
[48]
Webvoyager: Building an end-to-end web agent with large multimodal models
Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hong- ming Zhang, Zhenzhong Lan, and Dong Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume ...
2024
-
[49]
Camels can use computers too: System-level security for computer use agents.arXiv preprint arXiv:2601.09923, 2026
Hanna Foerster, Robert Mullins, Tom Blanchard, Nicolas Papernot, Kristina Nikoli ´c, Florian Tram `er, Ilia Shumailov, Cheng Zhang, and Yiren Zhao. Camels can use computers too: System-level security for computer use agents.arXiv preprint arXiv:2601.09923, 2026
2026
-
[50]
Riosworld: Benchmarking the risk of multimodal computer-use agents.arXiv preprint arXiv:2506.00618, 2025
Jingyi Yang, Shuai Shao, Dongrui Liu, and Jing Shao. Riosworld: Benchmarking the risk of multimodal computer-use agents.arXiv preprint arXiv:2506.00618, 2025
2025
-
[51]
Ferret-ui: Grounded mobile ui understanding with multimodal llms
Keen You, Haotian Zhang, Eldon Schoop, Floris Weers, Amanda Swearngin, Jeffrey Nichols, Yinfei Yang, and Zhe Gan. Ferret-ui: Grounded mobile ui understanding with multimodal llms. InEuropean Conference on Computer Vision, pages 240–255. Springer, 2024
2024
-
[52]
Trishul: Towards region identification and screen hierarchy understanding for large vlm based gui agents
Kunal Singh, Shreyas Singh, and Mukund Khanna. Trishul: Towards region identification and screen hierarchy understanding for large vlm based gui agents. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 170–179, 2025
2025
-
[53]
Gui-bee: Align gui action grounding to novel environments via autonomous exploration
Yue Fan, Handong Zhao, Ruiyi Zhang, Yu Shen, Xin Eric Wang, and Gang Wu. Gui-bee: Align gui action grounding to novel environments via autonomous exploration. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33249– 33266, 2025
2025
-
[54]
Enhancing visual grounding for gui agents via self-evolutionary reinforcement learning.arXiv preprint arXiv:2505.12370, 2025
Xinbin Yuan, Jian Zhang, Kaixin Li, Zhuoxuan Cai, Lujian Yao, Jie Chen, Enguang Wang, Qibin Hou, Jinwei Chen, Peng-Tao Jiang, and Bo Li. Enhancing visual grounding for gui agents via self-evolutionary reinforcement learning.arXiv preprint arXiv:2505.12370, 2025
2025
-
[55]
Vimo: A generative visual gui world model for app agent.arXiv preprint arXiv:2504.13936, 2025
Dezhao Luo, Bohan Tang, Kang Li, Georgios Papoudakis, Jifei Song, Shaogang Gong, Jianye Hao, Jun Wang, and Kun Shao. Vimo: A generative visual gui world model for app agent.arXiv preprint arXiv:2504.13936, 2025
2025
-
[56]
Secagent: Efficient mobile gui agent with semantic context.arXiv preprint arXiv:2603.08533, 2026
Yiping Xie, Song Chen, Jingxuan Xing, Wei Jiang, Zekun Zhu, Yingyao Wang, Pi Bu, Jun Song, Yuning Jiang, and Bo Zheng. Secagent: Efficient mobile gui agent with semantic context.arXiv preprint arXiv:2603.08533, 2026
2026
-
[57]
Webatlas: An llm agent with experience-driven memory and action simulation.arXiv preprint arXiv:2510.22732, 2025
Jiali Cheng, Anjishnu Kumar, Roshan Lal, Rishi Rajasekaran, Hani Ramezani, Omar Zia Khan, Oleg Rokhlenko, Sunny Chiu-Webster, Gang Hua, and Hadi Amiri. Webatlas: An llm agent with experience-driven memory and action simulation.arXiv preprint arXiv:2510.22732, 2025
2025
-
[58]
Litewebagent: The open-source suite for vlm-based web-agent applications
Danqing Zhang, Balaji Rama, Jingyi Ni, Shiying He, Fu Zhao, Kunyu Chen, Arnold Chen, and Junyu Cao. Litewebagent: The open-source suite for vlm-based web-agent applications. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Comp...
2025
-
[59]
A real-world webagent with planning, long context understanding, and program synthesis
Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust. A real-world webagent with planning, long context understanding, and program synthesis. arXiv preprint arXiv:2307.12856, 2023
2023
-
[60]
When openclaw ai agents teach each other: Peer learning patterns in the moltbook community.arXiv preprint arXiv:2602.14477, 2026
Eason Chen, Ce Guan, Ahmed Elshafiey, Zhonghao Zhao, Joshua Zekeri, Afeez Edeifo Shaibu, and Emmanuel Osadebe Prince. When openclaw ai agents teach each other: Peer learning patterns in the moltbook community.arXiv preprint arXiv:2602.14477, 2026
2026
-
[61]
Omniparser for pure vision based gui agent.arXiv preprint arXiv:2408.00203, 2024
Yadong Lu, Jianwei Yang, Yelong Shen, and Ahmed Awadal- lah. Omniparser for pure vision based gui agent.arXiv preprint arXiv:2408.00203, 2024
2024
-
[62]
Winclick: Gui grounding with multimodal large language models.arXiv preprint arXiv:2503.04730, 2025
Zheng Hui, Yinheng Li, Tianyi Chen, Colby Banbury, Kazuhito Koishida, et al. Winclick: Gui grounding with multimodal large language models.arXiv preprint arXiv:2503.04730, 2025
2025
-
[63]
Adazoom-gui: Adaptive zoom-based gui grounding with instruction refinement.arXiv preprint arXiv:2603.17441, 2026
Siqi Pei, Liang Tang, Tiaonan Duan, Long Chen, Shuxian Li, Kaer Huang, Yanzhe Jing, Yiqiang Yan, Bo Zhang, Chenghao Jiang, et al. Adazoom-gui: Adaptive zoom-based gui grounding with instruction refinement.arXiv preprint arXiv:2603.17441, 2026
2026
-
[64]
Synapse: Trajectory-as-exemplar prompting with memory for computer control
Longtao Zheng, Rundong Wang, Xinrun Wang, and Bo An. Synapse: Trajectory-as-exemplar prompting with memory for computer control. arXiv preprint arXiv:2306.07863, 2023
2023
-
[65]
Ufo: A ui-focused agent for windows os interaction
Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, et al. Ufo: A ui-focused agent for windows os interaction. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Comp...
2025
-
[66]
Ee-mcp: Self-evolving mcp-gui agents via automated environment generation and experience learning.arXiv preprint arXiv:2604.09815, 2026
Tiantian He, Yihang Chen, Keyue Jiang, Ka Yiu Lee, Kaiwen Zhou, Kun Shao, and Shuai Wang. Ee-mcp: Self-evolving mcp-gui agents via automated environment generation and experience learning.arXiv preprint arXiv:2604.09815, 2026
2026 arXiv
-
[67]
Treecua: Effi- ciently scaling gui automation with tree-structured verifiable evolution
Deyang Jiang, Jing Huang, Xuanle Zhao, Lei Chen, Liming Zheng, Fanfan Liu, Haibo Qiu, Peng Shi, and Zhixiong Zeng. Treecua: Effi- ciently scaling gui automation with tree-structured verifiable evolution. arXiv preprint arXiv:2602.09662, 2026
2026
-
[68]
Appagentx: Evolving gui agents as proficient smartphone users.arXiv preprint arXiv:2503.02268, 2025
Wenjia Jiang, Yangyang Zhuang, Chenxi Song, Xu Yang, Joey Tianyi Zhou, and Chi Zhang. Appagentx: Evolving gui agents as proficient smartphone users.arXiv preprint arXiv:2503.02268, 2025
2025
-
[69]
Gui-360◦: A comprehensive dataset and benchmark for computer-using agents.arXiv preprint arXiv:2511.04307, 2025
Jian Mu, Chaoyun Zhang, Chiming Ni, Lu Wang, Bo Qiao, Kartik Mathur, Qianhui Wu, Yuhang Xie, Xiaojun Ma, Mengyu Zhou, et al. Gui-360◦: A comprehensive dataset and benchmark for computer-using agents.arXiv preprint arXiv:2511.04307, 2025
2025
-
[70]
Showui-π: Flow-based generative models as gui dexterous hands.arXiv preprint arXiv:2512.24965, 2025
Siyuan Hu, Kevin Qinghong Lin, and Mike Zheng Shou. Showui-π: Flow-based generative models as gui dexterous hands.arXiv preprint arXiv:2512.24965, 2025
2025
-
[71]
Polyskill: Learning generalizable skills through polymorphic abstraction.arXiv preprint arXiv:2510.15863, 2025
Simon Yu, Gang Li, Weiyan Shi, and Peng Qi. Polyskill: Learning generalizable skills through polymorphic abstraction.arXiv preprint arXiv:2510.15863, 2025
2025
-
[72]
Seeclick: Harnessing gui grounding for advanced visual gui agents
Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Li YanTao, Jianbing Zhang, and Zhiyong Wu. Seeclick: Harnessing gui grounding for advanced visual gui agents. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p...
2024
-
[73]
Mobile-agent: Autonomous multi-modal mobile device agent with visual perception.arXiv preprint arXiv:2401.16158, 2024
Junyang Wang, Haiyang Xu, Jiabo Ye, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent: Autonomous multi-modal mobile device agent with visual perception.arXiv preprint arXiv:2401.16158, 2024
2024
-
[74]
Screenai: A vision-language model for ui and infographics understanding.arXiv preprint arXiv:2402.04615, 2024
Gilles Baechler, Srinivas Sunkara, Maria Wang, Fedir Zubach, Hassan Mansoor, Vincent Etter, Victor C˘arbune, Jason Lin, Jindong Chen, and Abhanshu Sharma. Screenai: A vision-language model for ui and infographics understanding.arXiv preprint arXiv:2402.04615, 2024
2024
-
[75]
Moving beyond sparse grounding with complete screen parsing supervision.arXiv preprint arXiv:2602.14276, 2026
A Said Gurbuz, Sunghwan Hong, Ahmed Nassar, Marc Pollefeys, and Peter Staar. Moving beyond sparse grounding with complete screen parsing supervision.arXiv preprint arXiv:2602.14276, 2026
2026 arXiv
-
[76]
Cogagent: A visual language model for gui agents
Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al. Cogagent: A visual language model for gui agents. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14281–14290, 2024
2024
-
[77]
Introducing our multimodal models, 2023
Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Au- gustus Odena, Arushi Somani, and Sa ˘gnak Tas ¸ırlar. Introducing our multimodal models, 2023
2023
-
[78]
A versatile vision-language model for understanding, localization, text reading, and beyond.arXiv preprint arXiv:2308.12966, 6:3, 2023
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and J Qwen-VL Zhou. A versatile vision-language model for understanding, localization, text reading, and beyond.arXiv preprint arXiv:2308.12966, 6:3, 2023
2023 arXiv
-
[79]
Afragent: An adaptive feature renormalization based high resolution aware gui agent
Neeraj Anand, Rishabh Jain, Sohan Patnaik, Balaji Krishnamurthy, and Mausoom Sarkar. Afragent: An adaptive feature renormalization based high resolution aware gui agent. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1147– 1158, 2026
2026
-
[80]
ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
Kaixin Li, Ziyang Meng, Hongzhan Lin, Ziyang Luo, Yuchen Tian, Jing Ma, Zhiyong Huang, and Tat-Seng Chua. ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use. In Proceedings of the 33rd ACM International Conference on Multimedia, pages 8778–8786. ACM, 2025
2025
-
[81]
Appagent: Multimodal agents as smartphone users
Chi Zhang, Zhao Yang, Jiaxuan Liu, Yanda Li, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. InProceedings of the 2025 CHI Confer- ence on Human Factors in Computing Systems, pages 1–20, 2025
2025
-
[82]
Infiguiagent: A multimodal generalist gui agent with native reasoning and reflection.arXiv preprint arXiv:2501.04575, 2025
Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, and Fei Wu. Infiguiagent: A multimodal generalist gui agent with native reasoning and reflection.arXiv preprint arXiv:2501.04575, 2025
2025
-
[83]
Androtmem: From interaction trajectories to anchored memory in long- horizon gui agents.arXiv preprint arXiv:2603.18429, 2026
Yibo Shi, Jungang Li, Linghao Zhang, Zihao Dongfang, Biao Wu, Sicheng Tao, Yibo Yan, Chenxi Qin, Weiting Liu, Zhixin Lin, et al. Androtmem: From interaction trajectories to anchored memory in long- horizon gui agents.arXiv preprint arXiv:2603.18429, 2026
2026
-
[84]
Gui-reflection: Empowering multimodal gui models with self-reflection behavior.arXiv preprint arXiv:2506.08012, 2025
Penghao Wu, Shengnan Ma, Bo Wang, Jiaheng Yu, Lewei Lu, and Ziwei Liu. Gui-reflection: Empowering multimodal gui models with self-reflection behavior.arXiv preprint arXiv:2506.08012, 2025
2025
-
[85]
Mobileuse: A gui agent with hierarchical reflection for autonomous mobile operation.arXiv preprint arXiv:2507.16853, 2025
Ning Li, Xiangmou Qu, Jiamu Zhou, Jun Wang, Muning Wen, Kouni- anhua Du, Xingyu Lou, Qiuying Peng, Jun Wang, and Weinan Zhang. Mobileuse: A gui agent with hierarchical reflection for autonomous mobile operation.arXiv preprint arXiv:2507.16853, 2025
2025
-
[86]
Lps-bench: Benchmarking safety awareness of computer-use agents in long-horizon planning under benign and adversarial scenarios
Tianyu Chen, Chujia Hu, Ge Gao, Dongrui Liu, Xia Hu, and Wenjie Wang. Lps-bench: Benchmarking safety awareness of computer-use agents in long-horizon planning under benign and adversarial scenarios. arXiv preprint arXiv:2602.03255, 2026
2026
-
[87]
When users change their mind: Evaluating interruptible agents in long-horizon web navigation.arXiv preprint arXiv:2604.00892, 2026
Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang, Yankai Chen, Yue Zhou, Hanrong Zhang, Yaozu Wu, Liancheng Fang, Zhengyao Gu, Zhen Zhang, et al. When users change their mind: Evaluating interruptible agents in long-horizon web navigation.arXiv preprint arXiv:2604.00892, 2026
2026
-
[88]
Guide: A benchmark for under- standing and assisting users in open-ended gui tasks.arXiv preprint arXiv:2603.25864, 2026
Saelyne Yang, Jaesang Yu, Yi-Hao Peng, Kevin Qinghong Lin, Jae Won Cho, Yale Song, and Juho Kim. Guide: A benchmark for under- standing and assisting users in open-ended gui tasks.arXiv preprint arXiv:2603.25864, 2026
2026
-
[89]
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. InThe eleventh international conference on learning representations, 2022
2022
-
[90]
Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023
2023
-
[91]
Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634– 8652, 2023
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634– 8652, 2023
2023
-
[92]
You only look at screens: Multimodal chain-of-action agents
Zhuosheng Zhang and Aston Zhang. You only look at screens: Multimodal chain-of-action agents. InFindings of the Association for Computational Linguistics: ACL 2024, pages 3132–3149, 2024
2024
-
[93]
Reinagent: A context-aware gui agent enabling human-in-the- loop mobile task navigation.arXiv preprint arXiv:2510.07988, 2025
Haitao Jia, Ming He, Zimo Yin, Likang Wu, Jianping Fan, and Jitao Sang. Reinagent: A context-aware gui agent enabling human-in-the- loop mobile task navigation.arXiv preprint arXiv:2510.07988, 2025
2025
-
[94]
Backtrackagent: Enhancing gui agent with error detection and backtracking mechanism
Qinzhuo Wu, Pengzhi Gao, Wei Liu, and Jian Luan. Backtrackagent: Enhancing gui agent with error detection and backtracking mechanism. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4250–4272, 2025
2025
-
[95]
Building a stable planner: An extended finite state machine based planning module for mobile gui agent.arXiv preprint arXiv:2505.14141, 2025
Fanglin Mo, Junzhe Chen, Haoxuan Zhu, and Xuming Hu. Building a stable planner: An extended finite state machine based planning module for mobile gui agent.arXiv preprint arXiv:2505.14141, 2025
2025
-
[96]
Don’t act blindly: Robust gui automation via action-effect verification and self-correction.arXiv preprint arXiv:2604.05477, 2026
Yuzhe Zhang, Xianwei Xue, Xingyong Wu, Mengke Chen, Chen Liu, Xinran He, Run Shao, Feiran Liu, Huanmin Xu, Qiutong Pan, et al. Don’t act blindly: Robust gui automation via action-effect verification and self-correction.arXiv preprint arXiv:2604.05477, 2026
2026 arXiv
-
[97]
Agent-sama: State-aware mobile assistant
Linqiang Guo, Wei Liu, Yi Wen Heng, Tse-Hsun Peter Chen, and Yang Wang. Agent-sama: State-aware mobile assistant. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 29459– 29467, 2026
2026
-
[98]
Cradle: Empowering foundation agents towards general computer control.arXiv preprint arXiv:2403.03186, 2024
Weihao Tan, Wentao Zhang, Xinrun Xu, Haochong Xia, Ziluo Ding, Boyu Li, Bohan Zhou, Junpeng Yue, Jiechuan Jiang, Yewen Li, et al. Cradle: Empowering foundation agents towards general computer control.arXiv preprint arXiv:2403.03186, 2024
2024
-
[99]
Androidenv: A reinforcement learning platform for android.arXiv preprint arXiv:2105.13231, 2021
Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, and Doina Precup. Androidenv: A reinforcement learning platform for android.arXiv preprint arXiv:2105.13231, 2021
2021
-
[100]
Ui-tars-2 technical report: Advancing gui agent with multi-turn reinforcement learning.arXiv preprint arXiv:2509.02544, 2025
Haoming Wang, Haoyang Zou, Huatong Song, Jiazhan Feng, Junjie Fang, Junting Lu, Longxiang Liu, Qinyu Luo, Shihao Liang, Shijue Huang, et al. Ui-tars-2 technical report: Advancing gui agent with multi-turn reinforcement learning.arXiv preprint arXiv:2509.02544, 2025
2025
-
[101]
Screenagent: A vi- sion language model-driven computer control agent.arXiv preprint arXiv:2402.07945, 2024
Runliang Niu, Jindong Li, Shiqi Wang, Yali Fu, Xiyu Hu, Xueyuan Leng, He Kong, Yi Chang, and Qi Wang. Screenagent: A vi- sion language model-driven computer control agent.arXiv preprint arXiv:2402.07945, 2024
2024
-
[102]
Omegause: Building a general-purpose gui agent for autonomous task execution
Le Zhang, Yixiong Xiao, Xinjiang Lu, Jingjia Cao, Yusai Zhao, Jingbo Zhou, Lang An, Zikan Feng, Wanxiang Sha, Yu Shi, et al. Omegause: Building a general-purpose gui agent for autonomous task execution. arXiv preprint arXiv:2601.20380, 2026
2026
-
[103]
macosworld: A multilingual interactive benchmark for gui agents.arXiv preprint arXiv:2506.04135, 2025
Pei Yang, Hai Ci, and Mike Zheng Shou. macosworld: A multilingual interactive benchmark for gui agents.arXiv preprint arXiv:2506.04135, 2025
2025
-
[104]
Websp-eval: Evaluating web agents on website security and privacy tasks.arXiv preprint arXiv:2604.06367, 2026
Guruprasad Viswanathan Ramesh, Asmit Nayak, Basieem Siddique, and Kassem Fawaz. Websp-eval: Evaluating web agents on website security and privacy tasks.arXiv preprint arXiv:2604.06367, 2026
2026 arXiv
-
[105]
Turing test on screen: A benchmark for mobile gui agent humanization.arXiv preprint arXiv:2604.09574, 2026
Jiachen Zhu, Lingyu Yang, Rong Shan, Congmin Zheng, Zeyu Zheng, Weiwen Liu, Yong Yu, Weinan Zhang, and Jianghao Lin. Turing test on screen: A benchmark for mobile gui agent humanization.arXiv preprint arXiv:2604.09574, 2026
2026 arXiv
-
[106]
Modeling distinct human interaction in web agents.arXiv preprint arXiv:2602.17588, 2026
Faria Huq, Zora Zhiruo Wang, Zhanqiu Guo, Venu Arvind Arangarajan, Tianyue Ou, Frank Xu, Shuyan Zhou, Graham Neubig, and Jeffrey P Bigham. Modeling distinct human interaction in web agents.arXiv preprint arXiv:2602.17588, 2026
2026
-
[107]
Androidinthewild: A large-scale dataset for android device control.Advances in Neural Information Processing Systems, 36:59708–59728, 2023
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, and Timothy Lillicrap. Androidinthewild: A large-scale dataset for android device control.Advances in Neural Information Processing Systems, 36:59708–59728, 2023
2023
-
[108]
Agenttuning: Enabling generalized agent abilities for llms
Aohan Zeng, Mingdao Liu, Rui Lu, Bowen Wang, Xiao Liu, Yuxiao Dong, and Jie Tang. Agenttuning: Enabling generalized agent abilities for llms. InFindings of the Association for Computational Linguistics: ACL 2024, pages 3053–3077, 2024
2024
-
[109]
Tongui: Internet-scale trajectories from multimodal web tutorials for general- ized gui agents.arXiv preprint arXiv:2504.12679, 2025
Bofei Zhang, Zirui Shang, Zhi Gao, Wang Zhang, Rui Xie, Xiaojian Ma, Tao Yuan, Xinxiao Wu, Song-Chun Zhu, and Qing Li. Tongui: Internet-scale trajectories from multimodal web tutorials for general- ized gui agents.arXiv preprint arXiv:2504.12679, 2025
2025
-
[110]
Webchain: A large-scale human- annotated dataset of real-world web interaction traces.arXiv preprint arXiv:2603.05295, 2026
Sicheng Fan, Rui Wan, Yifei Leng, Gaoning Liang, Li Ling, Yanyi Shang, and Dehan Kong. Webchain: A large-scale human- annotated dataset of real-world web interaction traces.arXiv preprint arXiv:2603.05295, 2026
2026 arXiv
-
[111]
Os-genesis: Automating gui agent trajectory construction via reverse task synthesis
Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, et al. Os-genesis: Automating gui agent trajectory construction via reverse task synthesis. InProceedings of the 63rd Annual Meeting of the Associ...
2025
-
[112]
Ui-r1: Enhancing efficient action prediction of gui agents by reinforcement learning.arXiv preprint arXiv:2503.21620, 2025
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Han Xiao, Shuai Ren, Guanjing Xiong, and Hongsheng Li. Ui-r1: Enhancing efficient action prediction of gui agents by reinforcement learning.arXiv preprint arXiv:2503.21620, 2025
2025
-
[113]
Gui-r1: A generalist r1-style vision-language action model for gui agents.arXiv preprint arXiv:2504.10458, 2025
Run Luo, Lu Wang, Wanwei He, Longze Chen, Jiaming Li, and Xiaobo Xia. Gui-r1: A generalist r1-style vision-language action model for gui agents.arXiv preprint arXiv:2504.10458, 2025
2025
-
[114]
Progrm: Build better gui agents with progress rewards.arXiv preprint arXiv:2505.18121, 2025
Danyang Zhang, Situo Zhang, Ziyue Yang, Zichen Zhu, Zihan Zhao, Ruisheng Cao, Lu Chen, and Kai Yu. Progrm: Build better gui agents with progress rewards.arXiv preprint arXiv:2505.18121, 2025
2025
-
[115]
Web-shepherd: Advancing prms for reinforcing web agents.arXiv preprint arXiv:2505.15277, 2025
Hyungjoo Chae, Sunghwan Kim, Junhee Cho, Seungone Kim, Se- ungjun Moon, Gyeom Hwangbo, Dongha Lim, Minjin Kim, Yeonjun Hwang, Minju Gwak, et al. Web-shepherd: Advancing prms for reinforcing web agents.arXiv preprint arXiv:2505.15277, 2025
2025
-
[116]
Ui-genie: A self-improving approach for iteratively boosting mllm-based mobile gui agents.arXiv preprint arXiv:2505.21496, 2025
Han Xiao, Guozhi Wang, Yuxiang Chai, Zimu Lu, Weifeng Lin, Hao He, Lue Fan, Liuyang Bian, Rui Hu, Liang Liu, et al. Ui-genie: A self-improving approach for iteratively boosting mllm-based mobile gui agents.arXiv preprint arXiv:2505.21496, 2025
2025
-
[117]
Magicgui- rms: A multi-agent reward model system for self-evolving gui agents via automated feedback reflux.arXiv preprint arXiv:2601.13060, 2026
Zecheng Li, Zhihui Cao, Wenke Huang, Yudong Zhang, Keying Qi, Rui Wang, Zeyu Zheng, Jian Zhao, Hao Zhu, Hengxin Wu, et al. Magicgui- rms: A multi-agent reward model system for self-evolving gui agents via automated feedback reflux.arXiv preprint arXiv:2601.13060, 2026
2026
-
[118]
Video- based reward modeling for computer-use agents.arXiv preprint arXiv:2603.10178, 2026
Linxin Song, Jieyu Zhang, Huanxin Sheng, Taiwei Shi, Gupta Rahul, Yang Liu, Ranjay Krishna, Jian Kang, and Jieyu Zhao. Video- based reward modeling for computer-use agents.arXiv preprint arXiv:2603.10178, 2026
2026
-
[119]
Mcp security bench (msb): Benchmarking attacks against model context protocol in llm agents.arXiv preprint arXiv:2510.15994, 2025
Dongsen Zhang, Zekun Li, Xu Luo, Xuannan Liu, Peipei Li, and Wenjun Xu. Mcp security bench (msb): Benchmarking attacks against model context protocol in llm agents.arXiv preprint arXiv:2510.15994, 2025
2025
-
[120]
Les dissonances: Cross-tool harvesting and polluting in pool-of-tools empowered llm agents.arXiv preprint arXiv:2504.03111, 2025
Zichuan Li, Jian Cui, Xiaojing Liao, and Luyi Xing. Les dissonances: Cross-tool harvesting and polluting in pool-of-tools empowered llm agents.arXiv preprint arXiv:2504.03111, 2025
2025
-
[121]
Worldgui: An interactive benchmark for desktop gui automation from any starting point.arXiv preprint arXiv:2502.08047, 2025
Henry Hengyuan Zhao, Kaiming Yang, Wendi Yu, Difei Gao, and Mike Zheng Shou. Worldgui: An interactive benchmark for desktop gui automation from any starting point.arXiv preprint arXiv:2502.08047, 2025
2025
-
[122]
Actio- nengine: From reactive to programmatic gui agents via state machine memory.arXiv preprint arXiv:2602.20502, 2026
Hongbin Zhong, Fazle Faisal, Luis Franc ¸a, Tanakorn Leesataporn- wongsa, Adriana Szekeres, Kexin Rong, and Suman Nath. Actio- nengine: From reactive to programmatic gui agents via state machine memory.arXiv preprint arXiv:2602.20502, 2026
2026
-
[123]
The amazing agent race: Strong tool users, weak navigators.arXiv preprint arXiv:2604.10261, 2026
Zae Myung Kim, Dongseok Lee, Jaehyung Kim, Vipul Raheja, and Dongyeop Kang. The amazing agent race: Strong tool users, weak navigators.arXiv preprint arXiv:2604.10261, 2026
2026 arXiv
-
[124]
Mobileworldbench: Towards semantic world modeling for mobile agents.arXiv preprint arXiv:2512.14014, 2025
Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, Kazuki Kozuka, and Aditya Grover. Mobileworldbench: Towards semantic world modeling for mobile agents.arXiv preprint arXiv:2512.14014, 2025
2025
-
[125]
Webinject: Prompt injection attack to web agents
Xilong Wang, John Bloch, Zedian Shao, Yuepeng Hu, Shuyan Zhou, and Neil Zhenqiang Gong. Webinject: Prompt injection attack to web agents. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 2010–2030, 2025
2025
-
[126]
In-context defense in computer agents: An empirical study.arXiv preprint arXiv:2503.09241, 2025
Pei Yang, Hai Ci, and Mike Zheng Shou. In-context defense in computer agents: An empirical study.arXiv preprint arXiv:2503.09241, 2025
2025
-
[127]
Evaluating the robustness of multimodal agents against active environmental injection attacks
Yurun Chen, Xueyu Hu, Keting Yin, Juncheng Li, and Shengyu Zhang. Evaluating the robustness of multimodal agents against active environmental injection attacks. InProceedings of the 33rd ACM International Conference on Multimedia, pages 11648–11656, 2025
2025
-
[128]
From assistant to double agent: Formalizing and benchmarking at- tacks on openclaw for personalized local ai agent.arXiv preprint arXiv:2602.08412, 2026
Yuhang Wang, Feiming Xu, Zheng Lin, Guangyu He, Yuzhe Huang, Haichang Gao, Zhenxing Niu, Shiguo Lian, and Zhaoxiang Liu. From assistant to double agent: Formalizing and benchmarking at- tacks on openclaw for personalized local ai agent.arXiv preprint arXiv:2602.08412, 2026
2026
-
[129]
Your agent, their asset: A real-world safety analysis of openclaw.arXiv preprint arXiv:2604.04759, 2026
Zijun Wang, Haoqin Tu, Letian Zhang, Hardy Chen, Juncheng Wu, Xiangyan Liu, Zhenlong Yuan, Tianyu Pang, Michael Qizhe Shieh, Fengze Liu, et al. Your agent, their asset: A real-world safety analysis of openclaw.arXiv preprint arXiv:2604.04759, 2026
2026 arXiv
-
[130]
Cora: Conformal risk- controlled agents for safeguarded mobile gui automation.arXiv preprint arXiv:2604.09155, 2026
Yushi Feng, Junye Du, Qifan Wang, Zizhan Ma, Qian Niu, Yu- taka Matsuo, Long Feng, and Lequan Yu. Cora: Conformal risk- controlled agents for safeguarded mobile gui automation.arXiv preprint arXiv:2604.09155, 2026
2026 arXiv
-
[131]
Magnet: Towards adaptive gui agents with memory-driven knowledge evolution
Libo Sun, Jiwen Zhang, Siyuan Wang, and Zhongyu Wei. Magnet: Towards adaptive gui agents with memory-driven knowledge evolution. arXiv preprint arXiv:2601.19199, 2026
2026
-
[132]
Ui-oceanus: Scaling gui agents with synthetic environmental dynamics.arXiv preprint arXiv:2604.02345, 2026
Mengzhou Wu, Yuzhe Guo, Yuan Cao, Haochuan Lu, Songhe Zhu, Pingzhe Qu, Xin Chen, Kang Qin, Zhongpu Wang, Xiaode Zhang, et al. Ui-oceanus: Scaling gui agents with synthetic environmental dynamics.arXiv preprint arXiv:2604.02345, 2026
2026 arXiv
-
[133]
Memgui-bench: Benchmarking memory of mobile gui agents in dynamic environments.arXiv preprint arXiv:2602.06075, 2026
Guangyi Liu, Pengxiang Zhao, Yaozhen Liang, Qinyi Luo, Shunye Tang, Yuxiang Chai, Weifeng Lin, Han Xiao, WenHao Wang, Siheng Chen, et al. Memgui-bench: Benchmarking memory of mobile gui agents in dynamic environments.arXiv preprint arXiv:2602.06075, 2026
2026
-
[134]
TimeWarp: Evaluating Web Agents by Revisiting the Past, mar 2026
Md Farhan Ishmam and Kenneth Marino. TimeWarp: Evaluating Web Agents by Revisiting the Past, mar 2026. arXiv preprint arXiv:2603.04949
2026
-
[135]
Vision2web: A hierarchical benchmark for visual website development with agent verification.arXiv preprint arXiv:2603.26648, 2026
Zehai He, Wenyi Hong, Zhen Yang, Ziyang Pan, Mingdao Liu, Xiaotao Gu, and Jie Tang. Vision2web: A hierarchical benchmark for visual website development with agent verification.arXiv preprint arXiv:2603.26648, 2026
2026
-
[136]
Webtest- bench: Evaluating computer-use agents towards end-to-end automated web testing.arXiv preprint arXiv:2603.25226, 2026
Fanheng Kong, Jingyuan Zhang, Yang Yue, Chenxi Sun, Yang Tian, Shi Feng, Xiaocui Yang, Daling Wang, Yu Tian, Jun Du, et al. Webtest- bench: Evaluating computer-use agents towards end-to-end automated web testing.arXiv preprint arXiv:2603.25226, 2026
2026
-
[137]
Guitester: Enabling gui agents for exploratory defect discovery.arXiv preprint arXiv:2601.04500, 2026
Yifei Gao, Jiang Wu, Xiaoyi Chen, Yifan Yang, Zhe Cui, Tianyi Ma, Jiaming Zhang, and Jitao Sang. Guitester: Enabling gui agents for exploratory defect discovery.arXiv preprint arXiv:2601.04500, 2026
2026
-
[138]
Opeflo: Automated ux evaluation via simulated human web interaction with gui grounding.arXiv preprint arXiv:2604.09581, 2026
Wee Joe Tan, Zi Rui Lucas Lim, Shashank Durgad, Karim Obegi, and Aiden Yiliu Li. Opeflo: Automated ux evaluation via simulated human web interaction with gui grounding.arXiv preprint arXiv:2604.09581, 2026
2026 arXiv
-
[139]
Clawkeeper: Comprehensive safety protection for openclaw agents through skills, plugins, and watchers.arXiv preprint arXiv:2603.24414, 2026
Songyang Liu, Chaozhuo Li, Chenxu Wang, Jinyu Hou, Zejian Chen, Litian Zhang, Zheng Liu, Qiwei Ye, Yiming Hei, Xi Zhang, et al. Clawkeeper: Comprehensive safety protection for openclaw agents through skills, plugins, and watchers.arXiv preprint arXiv:2603.24414, 2026
2026
-
[140]
Vpi-bench: Visual prompt injection attacks for computer-use agents.arXiv preprint arXiv:2506.02456, 2025
Tri Cao, Bennett Lim, Yue Liu, Yuan Sui, Yuexin Li, Shumin Deng, Lin Lu, Nay Oo, Shuicheng Yan, and Bryan Hooi. Vpi-bench: Visual prompt injection attacks for computer-use agents.arXiv preprint arXiv:2506.02456, 2025
2025
-
[141]
Agentrae: Remote action execution through notification-based visual backdoors against screenshots-based mobile gui agents.arXiv preprint arXiv:2603.23007, 2026
Yutao Luo, Haotian Zhu, Shuchao Pang, Zhigang Lu, Tian Dong, Yongbin Zhou, and Minhui Xue. Agentrae: Remote action execution through notification-based visual backdoors against screenshots-based mobile gui agents.arXiv preprint arXiv:2603.23007, 2026
2026
-
[142]
Safepred: A predictive guardrail for computer-using agents via world models.arXiv preprint arXiv:2602.01725, 2026
Yurun Chen, Zeyi Liao, Ping Yin, Taotao Xie, Keting Yin, and Shengyu Zhang. Safepred: A predictive guardrail for computer-using agents via world models.arXiv preprint arXiv:2602.01725, 2026
2026
-
[143]
Permission manifests for web agents.arXiv preprint arXiv:2601.02371, 2025
Samuele Marro, Alan Chan, Xinxing Ren, Lewis Hammond, Jesse Wright, Gurjyot Wanga, Tiziano Piccardi, Nuno Campos, Tobin South, Jialin Yu, et al. Permission manifests for web agents.arXiv preprint arXiv:2601.02371, 2025
2025
-
[144]
Decepticon: How dark patterns manipulate web agents.arXiv preprint arXiv:2512.22894, 2025
Phil Cuvin, Hao Zhu, and Diyi Yang. Decepticon: How dark patterns manipulate web agents.arXiv preprint arXiv:2512.22894, 2025
2025
-
[145]
Slowba: An efficiency backdoor attack towards vlm-based gui agents.arXiv preprint arXiv:2603.08316, 2026
Junxian Li, Tu Lan, Haozhen Tan, Yan Meng, and Haojin Zhu. Slowba: An efficiency backdoor attack towards vlm-based gui agents.arXiv preprint arXiv:2603.08316, 2026
2026
-
[146]
Agentsentinel: An end-to-end and real-time security defense framework for computer- use agents
Haitao Hu, Peng Chen, Yanpeng Zhao, and Yuqi Chen. Agentsentinel: An end-to-end and real-time security defense framework for computer- use agents. InProceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, pages 3535–3549, 2025
2025
-
[147]
Mip against agent: Malicious image patches hijacking multimodal os agents.arXiv preprint arXiv:2503.10809, 2025
Lukas Aichberger, Alasdair Paren, Guohao Li, Philip Torr, Yarin Gal, and Adel Bibi. Mip against agent: Malicious image patches hijacking multimodal os agents.arXiv preprint arXiv:2503.10809, 2025
2025
-
[148]
Websentinel: Detecting and localizing prompt injection attacks for web agents.arXiv preprint arXiv:2602.03792, 2026
Xilong Wang, Yinuo Liu, Zhun Wang, Dawn Song, and Neil Gong. Websentinel: Detecting and localizing prompt injection attacks for web agents.arXiv preprint arXiv:2602.03792, 2026
2026
-
[149]
Bradley Knox, and Kimin Lee
Juyong Lee, Dongyoon Hahm, June Suk Choi, W. Bradley Knox, and Kimin Lee. MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control. InFortieth AAAI Conference on Artificial Intelligence, pages 37565–37573. AAAI Press, 2026
2026
-
[150]
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
Ido Levy, Ben Wiesel, Sami Marreed, Alon Oved, Avi Yaeli, Nir Mashkif, and Segev Shlomov. ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents. InInternational Conference on Learning Representations, 2026. Poster
2026
-
[151]
The blind spot of agent safety: How benign user instructions expose critical vulnerabilities in computer-use agents.arXiv preprint arXiv:2604.10577, 2026
Xuwei Ding, Skylar Zhai, Linxin Song, Jiate Li, Taiwei Shi, Nicholas Meade, Siva Reddy, Jian Kang, and Jieyu Zhao. The blind spot of agent safety: How benign user instructions expose critical vulnerabilities in computer-use agents.arXiv preprint arXiv:2604.10577, 2026
2026 arXiv
-
[152]
Safesearch: Automated red-teaming for the safety of llm-based search agents.arXiv preprint arXiv:2509.23694, 2025
Jianshuo Dong, Sheng Guo, Hao Wang, Xun Chen, Zhuotao Liu, Tianwei Zhang, Ke Xu, Minlie Huang, and Han Qiu. Safesearch: Automated red-teaming for the safety of llm-based search agents.arXiv preprint arXiv:2509.23694, 2025
2025
-
[153]
A systematization of security vulnerabilities in computer use agents.arXiv preprint arXiv:2507.05445, 2025
Daniel Jones, Giorgio Severi, Martin Pouliot, Gary Lopez, Joris de Gruyter, Santiago Zanella-Beguelin, Justin Song, Blake Bull- winkel, Pamela Cortez, and Amanda Minnich. A systematization of security vulnerabilities in computer use agents.arXiv preprint arXiv:2507.05445, 2025
2025
-
[154]
Agent security bench (asb): Formalizing and benchmarking attacks and defenses in llm-based agents.arXiv preprint arXiv:2410.02644, 2024
Hanrong Zhang, Jingyuan Huang, Kai Mei, Yifei Yao, Zhenting Wang, Chenlu Zhan, Hongwei Wang, and Yongfeng Zhang. Agent security bench (asb): Formalizing and benchmarking attacks and defenses in llm-based agents.arXiv preprint arXiv:2410.02644, 2024
2024
-
[155]
Evil geniuses: Delving into the safety of llm-based agents.arXiv preprint arXiv:2311.11855, 2023
Yu Tian, Xiao Yang, Jingyuan Zhang, Yinpeng Dong, and Hang Su. Evil geniuses: Delving into the safety of llm-based agents.arXiv preprint arXiv:2311.11855, 2023
2023
-
[156]
Hidden ghost hand: Unveiling backdoor vulnerabilities in mllm-powered mobile gui agents.arXiv preprint arXiv:2505.14418, 2025
Pengzhou Cheng, Haowen Hu, Zheng Wu, Zongru Wu, Tianjie Ju, Zhuosheng Zhang, and Gongshen Liu. Hidden ghost hand: Unveiling backdoor vulnerabilities in mllm-powered mobile gui agents.arXiv preprint arXiv:2505.14418, 2025
2025
-
[157]
Badvla: Towards backdoor attacks on vision-language- action models via objective-decoupled optimization.arXiv preprint arXiv:2505.16640, 2025
Xueyang Zhou, Guiyao Tie, Guowen Zhang, Hechang Wang, Pan Zhou, and Lichao Sun. Badvla: Towards backdoor attacks on vision-language- action models via objective-decoupled optimization.arXiv preprint arXiv:2505.16640, 2025
2025
-
[158]
Measuring harmfulness of computer-using agents
Aaron Xuxiang Tian, Ruofan Zhang, Janet Tang, Ji Wang, Tianyu Shi, and Jiaxin Wen. Measuring harmfulness of computer-using agents. arXiv preprint arXiv:2508.00935, 2025
2025
-
[159]
Secure and efficient access control for computer-use agents via context space
Haochen Gong, Chenxiao Li, Rui Chang, and Wenbo Shen. Secure and efficient access control for computer-use agents via context space. arXiv preprint arXiv:2509.22256, 2025
2025
-
[160]
cellmate: Sandboxing browser ai agents.arXiv preprint arXiv:2512.12594, 2025
Luoxi Meng, Henry Feng, Ilia Shumailov, and Earlence Fernan- des. cellmate: Sandboxing browser ai agents.arXiv preprint arXiv:2512.12594, 2025
2025
-
[161]
Redteamcua: Realistic adversarial testing of computer-use agents in hybrid web-os environments.arXiv preprint arXiv:2505.21936, 2025
Zeyi Liao, Jaylen Jones, Linxi Jiang, Yuting Ning, Eric Fosler-Lussier, Yu Su, Zhiqiang Lin, and Huan Sun. Redteamcua: Realistic adversarial testing of computer-use agents in hybrid web-os environments.arXiv preprint arXiv:2505.21936, 2025
2025
-
[162]
Hackworld: Evaluating computer-use agents on exploiting web application vulnerabilities.arXiv preprint arXiv:2510.12200, 2025
Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang, Xiaoning Du, Jiaojiao Jiang, Zhenchang Xing, Jiamou Sun, and Terry Yue Zhuo. Hackworld: Evaluating computer-use agents on exploiting web application vulnerabilities.arXiv preprint arXiv:2510.12200, 2025
2025
-
[163]
Injecagent: Benchmarking indirect prompt injections in tool-integrated large lan- guage model agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large lan- guage model agents. InFindings of the Association for Computational Linguistics: ACL 2024, pages 10471–10506, 2024
2024
-
[164]
Ghostei-bench: Do mobile agents resilience to environmental injection in dynamic on-device environments?arXiv preprint arXiv:2510.20333, 2025
Chiyu Chen, Xinhao Song, Yunkai Chai, Yang Yao, Haodong Zhao, Lijun Li, Jie Li, Yan Teng, Gongshen Liu, and Yingchun Wang. Ghostei-bench: Do mobile agents resilience to environmental injection in dynamic on-device environments?arXiv preprint arXiv:2510.20333, 2025
2025
-
[165]
Environmental injection attacks against gui agents in realistic dynamic environments.arXiv preprint arXiv:2509.11250, 2026
Yitong Zhang, Ximo Li, Liyi Cai, and Jia Li. Environmental injection attacks against gui agents in realistic dynamic environments.arXiv preprint arXiv:2509.11250, 2026
2026
-
[166]
Wasp: Benchmarking web agent security against prompt injection attacks.arXiv preprint arXiv:2504.18575, 2025
Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, and Kamalika Chaudhuri. Wasp: Benchmarking web agent security against prompt injection attacks.arXiv preprint arXiv:2504.18575, 2025
2025
-
[167]
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage
Zeyi Liao, Lingbo Mo, Chejian Xu, Mintong Kang, Jiawei Zhang, Chaowei Xiao, Yuan Tian, Bo Li, and Huan Sun. EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage. In International Conference on Learning Representations, 2025. Poster
2025
-
[168]
Are gui agents focused enough? automated distraction via semantic-level ui element injection.arXiv preprint arXiv:2604.07831, 2026
Wenkui Yang, Chao Jin, Haisu Zhu, Weilin Luo, Derek Yuen, Kun Shao, Huaibo Huang, Junxian Duan, Jie Cao, and Ran He. Are gui agents focused enough? automated distraction via semantic-level ui element injection.arXiv preprint arXiv:2604.07831, 2026
2026 arXiv
-
[169]
It’s a trap! task-redirecting agent persuasion benchmark for web agents.arXiv preprint arXiv:2512.23128, 2025
Karolina Korgul, Yushi Yang, Arkadiusz Drohomirecki, Will Howard, Lukas Aichberger, Chris Russell, Philip HS Torr, Adam Mahdi, Adel Bibi, et al. It’s a trap! task-redirecting agent persuasion benchmark for web agents.arXiv preprint arXiv:2512.23128, 2025
2025
-
[170]
Investi- gating the impact of dark patterns on llm-based web agents.arXiv preprint arXiv:2510.18113, 2025
Devin Ersoy, Brandon Lee, Ananth Shreekumar, Arjun Arunasalam, Muhammad Ibrahim, Antonio Bianchi, and Z Berkay Celik. Investi- gating the impact of dark patterns on llm-based web agents.arXiv preprint arXiv:2510.18113, 2025
2025
-
[171]
Dual-modality multi-stage adversarial safety training: Robustifying multimodal web agents against cross-modal attacks.arXiv preprint arXiv:2603.04364, 2026
Haoyu Liu, Dingcheng Li, Lukas Rutishauser, and Zeyu Zheng. Dual-modality multi-stage adversarial safety training: Robustifying multimodal web agents against cross-modal attacks.arXiv preprint arXiv:2603.04364, 2026
2026
-
[172]
Agentdog: A diagnostic guardrail framework for ai agent safety and security.arXiv preprint arXiv:2601.18491, 2026
Dongrui Liu, Qihan Ren, Chen Qian, Shuai Shao, Yuejin Xie, Yu Li, Zhonghao Yang, Haoyu Luo, Peng Wang, Qingyu Liu, et al. Agentdog: A diagnostic guardrail framework for ai agent safety and security.arXiv preprint arXiv:2601.18491, 2026
2026 arXiv
-
[173]
Shieldagent: Shield- ing agents via verifiable safety policy reasoning.arXiv preprint arXiv:2503.22738, 2025
Zhaorun Chen, Mintong Kang, and Bo Li. Shieldagent: Shield- ing agents via verifiable safety policy reasoning.arXiv preprint arXiv:2503.22738, 2025
2025
-
[174]
Agentpoison: Red-teaming llm agents via poisoning memory or knowl- edge bases.Advances in Neural Information Processing Systems, 37:130185–130213, 2024
Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, and Bo Li. Agentpoison: Red-teaming llm agents via poisoning memory or knowl- edge bases.Advances in Neural Information Processing Systems, 37:130185–130213, 2024
2024
-
[175]
Preference redirection via attention concentration: An attack on computer use agents.arXiv preprint arXiv:2604.08005, 2026
Dominik Seip and Matthias Hein. Preference redirection via attention concentration: An attack on computer use agents.arXiv preprint arXiv:2604.08005, 2026
2026 arXiv
-
[176]
When benign inputs lead to severe harms: Eliciting unsafe unintended behaviors of computer-use agents.arXiv preprint arXiv:2602.08235, 2026
Jaylen Jones, Zhehao Zhang, Yuting Ning, Eric Fosler-Lussier, Pierre- Luc St-Charles, Yoshua Bengio, Dawn Song, Yu Su, and Huan Sun. When benign inputs lead to severe harms: Eliciting unsafe unintended behaviors of computer-use agents.arXiv preprint arXiv:2602.08235, 2026
2026
-
[177]
Zero-permission manipulation: Can we trust large multimodal model powered gui agents?arXiv preprint arXiv:2601.12349, 2026
Yi Qian, Kunwei Qian, Xingbang He, Ligeng Chen, Jikang Zhang, Tiantai Zhang, Haiyang Wei, Linzhang Wang, Hao Wu, and Bing Mao. Zero-permission manipulation: Can we trust large multimodal model powered gui agents?arXiv preprint arXiv:2601.12349, 2026
2026
-
[178]
Hijacking jarvis: Benchmarking mobile gui agents against unprivileged third parties
Guohong Liu, Jialei Ye, Jiacheng Liu, Yuanchun Li, Wei Liu, Pengzhi Gao, Jian Luan, and Yunxin Liu. Hijacking jarvis: Benchmarking mobile gui agents against unprivileged third parties. InProceedings of the 2nd International Workshop on Edge and Mobile Foundation Models, pages ...
2025
-
[179]
Prompt infection: Llm-to-llm prompt injection within multi-agent systems.arXiv preprint arXiv:2410.07283, 2024
Donghyun Lee and Mo Tiwari. Prompt infection: Llm-to-llm prompt injection within multi-agent systems.arXiv preprint arXiv:2410.07283, 2024
2024
-
[180]
Multi-agent security tax: Trading off security and collaboration capa- bilities in multi-agent systems
Pierre Peign ´e, Mikolaj Kniejski, Filip Sondej, Matthieu David, Jason Hoelscher-Obermaier, Christian Schroeder de Witt, and Esben Kran. Multi-agent security tax: Trading off security and collaboration capa- bilities in multi-agent systems. InProceedings of the AAAI Conference...
2025
-
[181]
Agentdam: Pri- vacy leakage evaluation for autonomous web agents.arXiv preprint arXiv:2503.09780, 2025
Arman Zharmagambetov, Chuan Guo, Ivan Evtimov, Maya Pavlova, Ruslan Salakhutdinov, and Kamalika Chaudhuri. Agentdam: Pri- vacy leakage evaluation for autonomous web agents.arXiv preprint arXiv:2503.09780, 2025
2025
-
[182]
Privacy in action: Towards realistic privacy mitigation and evaluation for llm-powered agents.arXiv preprint arXiv:2509.17488, 2025
Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, and Saravan Rajmohan. Privacy in action: Towards realistic privacy mitigation and evaluation for llm-powered agents.arXiv preprint arXiv:2509.17488, 2025
2025
-
[183]
Core: Reducing ui exposure in mobile agents via collaboration between cloud and local llms.arXiv preprint arXiv:2510.15455, 2025
Gucongcong Fan, Chaoyue Niu, Chengfei Lyu, Fan Wu, and Guihai Chen. Core: Reducing ui exposure in mobile agents via collaboration between cloud and local llms.arXiv preprint arXiv:2510.15455, 2025
2025
-
[184]
GUIGuard: Toward a General Framework for Privacy-Preserving GUI Agents, jan 2026
Yanxi Wang, Zhiling Zhang, Wenbo Zhou, Weiming Zhang, Jie Zhang, Qiannan Zhu, Yu Shi, Shuxin Zheng, and Jiyan He. GUIGuard: Toward a General Framework for Privacy-Preserving GUI Agents, jan 2026. arXiv preprint arXiv:2601.18842
2026
-
[185]
Veriguard: Enhancing llm agent safety via verified code generation.arXiv preprint arXiv:2510.05156, 2025
Lesly Miculicich, Mihir Parmar, Hamid Palangi, Krishnamurthy Dj Dvijotham, Mirko Montanari, Tomas Pfister, and Long T Le. Veriguard: Enhancing llm agent safety via verified code generation.arXiv preprint arXiv:2510.05156, 2025
2025
-
[186]
Mitigating indirect prompt injection via instruction- following intent analysis.arXiv preprint arXiv:2512.00966, 2025
Mintong Kang, Chong Xiang, Sanjay Kariyappa, Chaowei Xiao, Bo Li, and Edward Suh. Mitigating indirect prompt injection via instruction- following intent analysis.arXiv preprint arXiv:2512.00966, 2025
2025
-
[187]
When actions go off-task: Detecting and correcting misaligned actions in computer-use agents
Yuting Ning, Jaylen Jones, Zhehao Zhang, Chentao Ye, Weitong Ruan, Junyi Li, Rahul Gupta, and Huan Sun. When actions go off-task: Detecting and correcting misaligned actions in computer-use agents. arXiv preprint arXiv:2602.08995, 2026
2026
-
[188]
Mirrorguard: Toward secure computer-use agents via simulation-to-real reasoning correction.arXiv preprint arXiv:2601.12822, 2026
Wenqi Zhang, Yulin Shen, Changyue Jiang, Jiarun Dai, Geng Hong, and Xudong Pan. Mirrorguard: Toward secure computer-use agents via simulation-to-real reasoning correction.arXiv preprint arXiv:2601.12822, 2026
2026
-
[189]
VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification
Jungjae Lee, Dongjae Lee, Chihun Choi, Youngmin Im, Jaeyoung Wi, Kihong Heo, Sangeun Oh, Sunjae Lee, and Insik Shin. VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification. InProceedings of the 31st Annual International Conference on Mobile Computing...
2025
-
[190]
Gem: Gaussian embedding modeling for out-of- distribution detection in gui agents
Zheng Wu, Pengzhou Cheng, Zongru Wu, Lingzhong Dong, and Zhuosheng Zhang. Gem: Gaussian embedding modeling for out-of- distribution detection in gui agents. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 33989–33997, 2026
2026
-
[191]
Browsesafe: Understanding and preventing prompt injection within ai browser agents.arXiv preprint arXiv:2511.20597, 2025
Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley, Jerry Ma, Denis Yarats, and Ninghui Li. Browsesafe: Understanding and preventing prompt injection within ai browser agents.arXiv preprint arXiv:2511.20597, 2025
2025
-
[192]
Propen- sitybench: Evaluating latent safety risks in large language models via an agentic approach.arXiv preprint arXiv:2511.20703, 2025
Udari Madhushani Sehwag, Shayan Shabihi, Alex McAvoy, Vikash Sehwag, Yuancheng Xu, Dalton Towers, and Furong Huang. Propen- sitybench: Evaluating latent safety risks in large language models via an agentic approach.arXiv preprint arXiv:2511.20703, 2025
2025
Reviewed May 11, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.