4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Transferring Backdoors between Large Language Models by Knowledge Distillation
Pengzhou Cheng, Zongru Wu, Tianjie Ju +2
Backdoor Attacks have been a serious vulnerability against Large Language Models (LLMs). However, previous methods only reveal such risk in specific models, or present tasks transf…
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
Pengzhou Cheng, Yidong Ding, Tianjie Ju +5
Large language models (LLMs) have raised concerns about potential security threats despite performing significantly in Natural Language Processing (NLP). Backdoor attacks initially…
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Tianjie Ju, Weiwei Sun, Wei Du +3
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge. However, only limited research exists on…
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer
Pengzhou Cheng, Wei Du, Zongru Wu +4
Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms. These attacks can tran…
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Tianjie Ju, Yijin Chen, Xinwei Yuan +4
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning. However, the reliability of LLMs in combining these two capa…