3 citations · 16 across the 42 of their papers we have counts for
9 papers · 1 filter
AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
Zonghao Ying, Haozheng Wang, Jiangfan Liu +5
Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged execution exposes them to severe secu…
Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
Zonghao Ying, Xiao Yang, Siyang Wu +7
The rapid evolution of Large Language Models (LLMs) into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape. Frameworks like OpenClaw grant AI sy…
Evolving Deception: When Agents Evolve, Deception Wins
Zonghao Ying, Haowen Dai, Tianyuan Zhang +6
Self-evolving agents offer a promising path toward scalable autonomy. However, in this work, we show that in competitive environments, self-evolution can instead give rise to a ser…
SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
Zonghao Ying, Yangguang Shao, Jianle Gan +8
Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in real-world environments, they face…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions
Zonghao Ying, Le Wang, Yisong Xiao +7
The integration of vision-language models (VLMs) is driving a new generation of embodied agents capable of operating in human-centered environments. However, as deployment expands,…
Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings
Zonghao Ying, Guangyi Zheng, Yongxin Huang +6
This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their generated content. Our evaluatio…
SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models
Zonghao Ying, Aishan Liu, Siyuan Liang +5
Multimodal Large Language Models (MLLMs) are showing strong safety concerns (e.g., generating harmful outputs for users), which motivates the development of safety evaluation bench…
Unveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks
Zonghao Ying, Aishan Liu, Xianglong Liu +1
The recent release of GPT-4o has garnered widespread attention due to its powerful general capabilities. While its impressive performance is widely acknowledged, its safety aspects…