31 citations · 48 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
LSF-IDM: Automotive Intrusion Detection Model with Lightweight Attribution and Semantic Fusion
Pengzhou Cheng, Lei Hua, Haobin Jiang +1
Autonomous vehicles (AVs) are more vulnerable to network attacks due to the high connectivity and diverse communication modes between vehicles and external networks. Deep learning-…
Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review
Pengzhou Cheng, Zongru Wu, Wei Du +3
Language Models (LMs) are becoming increasingly popular in real-world applications. Outsourcing model training and data hosting to third-party platforms has become a standard metho…
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles
Pengzhou Cheng, Mu Han, Aoxue Li +1
Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertaint…