๐Ÿง Who I Am

๐Ÿ‘‹ Hi there! Iโ€™m a Ph.D. student at Zhejiang University, working under the guidance of Shengyu Zhang and Keting Yin. I also collaborate closely with Prof. Juncheng Li (Zhejiang University) and Prof. Zhuosheng Zhang (Shanghai Jiao Tong University).

๐Ÿ”ฌ My research focuses on LLM/Agent Security, specifically:

  • Adversarial Attacks, Knowledge Poisoning and Test-Time Attack targeting AI agents
  • Robust Agent and Safety Guardrails against emerging AI threats

๐Ÿ’ก Iโ€™m passionate about making AI systems more secure and trustworthy as they become increasingly integrated into our daily lives.

๐Ÿค If youโ€™re interested in my work or have ideas for collaboration, feel free to reach out via email.

๐Ÿ’ฌ News

[08/2025] ๐ŸŽ‰ Released github project "HarmonyGuard"!
[07/2025] ๐ŸŽ‰ Paper "Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks" accepted by ACM MM 2025!
[06/2025] ๐ŸŽ‰ Survey paper "OS Agents: A Survey on MLLM-based Agents for Use on General Computing Devices" selected as Oral Paper!
[04/2025] ๐ŸŽ‰ Survey paper "OS Agents: A Survey on MLLM-based Agents for Use on General Computing Devices" accepted by ACL 2025!
[02/2025] ๐ŸŽ‰ Published new work in arXiv: "Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks"!
[12/2024] ๐ŸŽ‰ Published paper in PrePrint: "OS Agents: A Survey on MLLM-based Agents for Computer, Phone, and Browser Use"!

๐Ÿ“ Publications

ACM MM 2025
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Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks

Yurun Chen, Xavier Hu, Keting Yin, Juncheng Li, Shengyu Zhang

The 33rd ACM International Conference on Multimedia (ACM MM 2025)

This work introduces Active Environment Injection Attacks (AEIA), where attackers disguise malicious inputs as environmental elements to manipulate AI agents' decisions. By analyzing Android OS interactions, the study reveals two key vulnerabilities and proposes the AEIA-MN attack, which achieves up to 93% success against advanced MLLM-based agents.

ACL 2025
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OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use

Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen etc.

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

This survey explores OS Agentsโ€”(M)LLM-based agents that operate within OS environments (e.g., GUI, CLI) on computers, phones, and browsers to automate tasks. It reviews their core components, construction methods, evaluation benchmarks, and outlines key challenges and future directions.

TMC S2A-P2FS: Secure Storage Auditing With Privacy-Preserving Flexible Data Sharing in Cloud-Assisted Industrial IoT

Xiaohu Shan; Haiyang Yu; Yurun Chen; Yuwen Chen; Zhen Yang

IEEE Transactions on Mobile Computing, 2025

View Paper
TSC EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage

Haiyang Yu, Yurun Chen, Zhen Yang, Yuwen Chen, Shui Yu

IEEE Transactions on Services Computing, 2024

View Paper

๐Ÿ“„ Preprints

arXiv
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HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive Policy Enhancement and Dual-Objective Optimization

Yurun Chen, Xavier Hu, Yuhan Liu, Keting Yin, Juncheng Li, Zhuosheng Zhang, Shengyu Zhang

We propose HarmonyGuard, a multi-agent collaborative framework that leverages policy enhancement and objective optimization to jointly improve both utility and safety in web agents. Extensive evaluations show that HarmonyGuard improves policy compliance by up to 38% and task completion by up to 20% over existing baselines, while achieving over 90% policy compliance across all tasks.

arXiv
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EcoAgent: An Efficient Edge-Cloud Collaborative Multi-Agent Framework for Mobile Automation

Biao Yi, Xavier Hu, Yurun Chen, Shengyu Zhang, Hongxia Yang, Fan Wu, Fei Wu

We propose EcoAgent, an Edge-Cloud Collaborative multi-agent framework for mobile automation that features a closed-loop collaboration among cloud-based Planning Agent and edge-based Execution and Observation Agents. Experiments on AndroidWorld show that EcoAgent achieves task success rates comparable to cloud-based mobile agents while significantly reducing MLLM token consumption, enabling efficient and practical mobile automation.

๐ŸŽ– Honors and Awards

  • 2025.06 Received the title of Outstanding Graduate of Beijing and the title of Top 100 Graduates of BJUT.
  • 2024.10 Received Xiaomi Scholarship.
  • 2024.09 Received National Scholarship.
  • 2023.08 Provincial Second Prize in the Graduate Electronic Design Competition.
  • 2023.02 Excellence Award in CCF&ATEC First Undergraduate Blockchain Security, Privacy Technology, and Innovative Application Competition.

๐Ÿ“– Educations

  • NOW, pursuing a Ph.D. at Zhejiang University (ZJU).
  • 2022.09 - 2025.06, Masterโ€™s Degree, Beijing University of Technology (BJUT).
  • 2019.09 - 2021.06 Bachelorโ€™s Degree, California State University, San Bernardino (CSUSB).
  • 2018.09 - 2022.06, Bachelorโ€™s Degree, Jiangsu University (JSU).

๐Ÿค Collaborators

  • Juncheng Li - Zhejiang University
  • Zhuosheng Zhang - Shanghai Jiao Tong University
  • Xavier Hu - Zhejiang University
  • Biao Yi - Zhejiang University
  • Yuhan Liu - Xiamen University

๐ŸŽฏ Reviewer Service

  • Conference Reviewer: ACLโ€™25, AAAIโ€™26.