31 citations · 48 across the 27 of their papers we have counts for
8 papers · 1 filter
Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining
Zongru Wu, Pengzhou Cheng, Lingyong Fang +2
Backdoor attacks remain significant security threats to generative large language models (LLMs). Since generative LLMs output sequences of high-dimensional token logits instead of…
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation
Haiyang Yu, Tian Xie, Jiaping Gui +3
Over the past few years, the emergence of backdoor attacks has presented significant challenges to deep learning systems, allowing attackers to insert backdoors into neural network…
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…
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
Tianjie Ju, Yiting Wang, Xinbei Ma +7
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solvi…
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…
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network
Pengzhou Cheng, Zongru Wu, Gongshen Liu
Control Area Network (CAN) is an essential communication protocol that interacts between Electronic Control Units (ECUs) in the vehicular network. However, CAN is facing stringent…
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…
Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
Zongru Wu, Zhuosheng Zhang, Pengzhou Cheng +1
Despite the notable success of language models (LMs) in various natural language processing (NLP) tasks, the reliability of LMs is susceptible to backdoor attacks. Prior research a…