3 citations · 5 across the 12 of their papers we have counts for
13 papers
JudgeStealer: Extracting LLM Judging Capabilities across Evaluation Protocols
Chen Chen, Yaolin Chen, Xuehan Sun +5
Large language model (LLM) judges are increasingly used across various evaluation scenarios, making their judgment capabilities valuable intellectual property. However, black-box a…
The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
Chen Chen, Xiang Gao, Xianshun Wang +6
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keepi…
SoK: Security and Privacy of Foundation-Model-Powered Robots
Xueluan Gong, Chen Chen, Jinxin Liu +2
Foundation models are reshaping robotics by enabling robots to interpret open-ended instructions, reason over multimodal contexts, and operate in complex, open-world environments.…
Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations
Chen Chen, Yuchen Sun, Jiaxin Gao +4
Large language models (LLMs) are increasingly deployed in security-sensitive applications, yet remain vulnerable to backdoor attacks. However, existing backdoor defenses are diffic…
The Shadow Self: Intrinsic Value Misalignment in Large Language Model Agents
Chen Chen, Kim Young Il, Yuan Yang +7
Large language model (LLM) agents with extended autonomy unlock new capabilities, but also introduce heightened challenges for LLM safety. In particular, an LLM agent may pursue ob…
Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems
Jiaxin Gao, Chen Chen, Yanwen Jia +3
Large Language Models (LLMs) are increasingly being used to autonomously evaluate the quality of content in communication systems, e.g., to assess responses in telecom customer sup…
Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution
Chen Chen, Yuchen Sun, Jiaxin Gao +5
Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks. However, they remain vulnerable…
LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Songze Li, Chuokun Xu, Jiaying Wang +6
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse tasks, driving the development and widespread adoption of LLM-as-a-Judge systems for automate…
Neutralizing Backdoors through Information Conflicts for Large Language Models
Chen Chen, Yuchen Sun, Xueluan Gong +2
Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks, from understanding to reasoning.…
Hidden Data Privacy Breaches in Federated Learning
Xueluan Gong, Yuji Wang, Shuaike Li +5
Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct d…
PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMs
Xueluan Gong, Mingzhe Li, Yilin Zhang +5
Large Language Models (LLMs) have excelled in various tasks but are still vulnerable to jailbreaking attacks, where attackers create jailbreak prompts to mislead the model to produ…
Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach
Bing Huang, Chen Chen, Kwok-Yan Lam +1
Emerging Internet of Things (IoT) platforms provide sophisticated capabilities to automate IoT services by enabling occupants to create trigger-action rules. Multiple trigger-actio…
A Survey on Conflict Detection in IoT-based Smart Homes
Bing Huang, Dipankar Chaki, Athman Bouguettaya +1
As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user…