5 citations · 8 across the 21 of their papers we have counts for
15 papers · 1 filter
DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack
Yuanjie Liu, Wenpeng Xing, Ye Zhou +3
The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing…
Dual Learning with Dynamic Knowledge Distillation and Soft Alignment for Partially Relevant Video Retrieval
Jianfeng Dong, Lei Huang, Daizong Liu +5
Almost all previous text-to-video retrieval works ideally assume that videos are pre-trimmed with short durations containing solely text-related content. However, in practice, vide…
CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor
Zhenhua Xu, Xixiang Zhao, Xubin Yue +3
The widespread deployment of large language models (LLMs) has intensified concerns around intellectual property (IP) protection, as model theft and unauthorized redistribution beco…
Web Fraud Attacks Against LLM-Driven Multi-Agent Systems
Dezhang Kong, Hujin Peng, Yilun Zhang +5
With the proliferation of LLM-driven multi-agent systems (MAS), the security of Web links has become a critical concern. Once MAS is induced to trust a malicious link, attackers ca…
CoDe-NeRF: Neural Rendering via Dynamic Coefficient Decomposition
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Neural Radiance Fields (NeRF) have shown impressive performance in novel view synthesis, but challenges remain in rendering scenes with complex specular reflections and highlights.…
UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like N…
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Zhenhua Xu, Xubin Yue, Zhebo Wang +9
Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys h…
SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth
Wenpeng Xing, Lanyi Wei, Haixiao Hu +5
The rapid proliferation of large language models (LLMs) in applications targeting children and adolescents necessitates a fundamental reassessment of prevailing AI safety framework…
MCP-Guard: A Multi-Stage Defense-in-Depth Framework for Securing Model Context Protocol in Agentic AI
Wenpeng Xing, Zhonghao Qi, Yupeng Qin +6
While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak. The integration of Large Language Models (LLMs) with external tools vi…
HGMF: A Hierarchical Gaussian Mixture Framework for Scalable Tool Invocation within the Model Context Protocol
Wenpeng Xing, Zhipeng Chen, Changting Lin +1
Invoking external tools enables Large Language Models (LLMs) to perform complex, real-world tasks, yet selecting the correct tool from large, hierarchically-structured libraries re…
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…
Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs
Hongming Yang, Shi Lin, Jun Shao +4
Lightweight Large Language Models (LwLLMs) are reduced-parameter, optimized models designed to run efficiently on consumer-grade hardware, offering significant advantages in resour…
A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
Dezhang Kong, Shi Lin, Zhenhua Xu +16
In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence and adaptability. Nowadays, agents are undergoing a new round of evolution. They no…
MIST: Jailbreaking Black-box Large Language Models via Iterative Semantic Tuning
Muyang Zheng, Yuanzhi Yao, Changting Lin +3
Despite efforts to align large language models (LLMs) with societal and moral values, these models remain susceptible to jailbreak attacks -- methods designed to elicit harmful res…
NeuRel-Attack: Neuron Relearning for Safety Disalignment in Large Language Models
Yi Zhou, Wenpeng Xing, Dezhang Kong +2
Safety alignment in large language models (LLMs) is achieved through fine-tuning mechanisms that regulate neuron activations to suppress harmful content. In this work, we propose a…