190 citations · 194 across the 7 of their papers we have counts for
5 papers · 1 filter
When Safety Routing Breaks: Understanding Alignment Fragility under Benign Fine-Tuning
Yitong Guo, Xiaoyi Chen, Siyuan Zhang +2
Benign fine-tuning severely weakens the safety alignment of large language models (LLMs), so we study why refusal behavior is so fragile. While prior work often attributes this fai…
The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
Xiaoyi Chen, Siyuan Tang, Rui Zhu +7
The rapid advancements of large language models (LLMs) have raised public concerns about the privacy leakage of personally identifiable information (PII) within their extensive tra…
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
Shengfang Zhai, Qingni Shen, Xiaoyi Chen +4
At present, backdoor attacks attract attention as they do great harm to deep learning models. The adversary poisons the training data making the model being injected with a backdoo…
Kallima: A Clean-label Framework for Textual Backdoor Attacks
Xiaoyi Chen, Yinpeng Dong, Zeyu Sun +3
Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to…
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements
Xiaoyi Chen, Ahmed Salem, Dingfan Chen +5
Deep neural networks (DNNs) have progressed rapidly during the past decade and have been deployed in various real-world applications. Meanwhile, DNN models have been shown to be vu…