most citedLLM-Virus: Evolutionary Jailbreak Attack on Large Language Models

2 citations · 4 across the 5 of their papers we have counts for

collaborators

11 papers

cs.CL2025

LIFEBench: Evaluating Length Instruction Following in Large Language Models

Wei Zhang, Zhenhong Zhou, Kun Wang +9

While large language models (LLMs) can solve PhD-level reasoning problems over long context inputs, they still struggle with a seemingly simpler task: following explicit length ins…

cs.CL2025

Goal-Aware Identification and Rectification of Misinformation in Multi-Agent Systems

Zherui Li, Yan Mi, Zhenhong Zhou +4

Large Language Model-based Multi-Agent Systems (MASs) have demonstrated strong advantages in addressing complex real-world tasks. However, due to the introduction of additional att…

cs.SD2025

AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models

Kai Li, Can Shen, Yile Liu +31

The rapid development and widespread adoption of Audio Large Language Models (ALLMs) demand rigorous evaluation of their trustworthiness. However, existing evaluation frameworks ar…

cs.CR2025

REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models

Chongye Guo, Jinhu Fu, Junfeng Fang +2

The rapid advancement of generative AI highlights the importance of text-to-image (T2I) security, particularly with the threat of backdoor poisoning. Timely disclosure and mitigati…

cs.AI2025

AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management

Junyuan Mao, Fanci Meng, Yifan Duan +6

Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized acce…

cs.CR20242 cited

LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models

Miao Yu, Junfeng Fang, Yingjie Zhou +4

While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems,…