5 papers
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…
ARMOR: Shielding Unlearnable Examples against Data Augmentation
Xueluan Gong, Yuji Wang, Yanjiao Chen +6
Private data, when published online, may be collected by unauthorized parties to train deep neural networks (DNNs). To protect privacy, defensive noises can be added to original sa…
A Survey on Facial Image Privacy Preservation in Cloud-Based Services
Chen Chen, Mengyuan Sun, Xueluan Gong +2
Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and su…
An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers
Xueluan Gong, Bowei Tian, Meng Xue +3
Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a…
Megatron: Evasive Clean-Label Backdoor Attacks against Vision Transformer
Xueluan Gong, Bowei Tian, Meng Xue +3
Vision transformers have achieved impressive performance in various vision-related tasks, but their vulnerability to backdoor attacks is under-explored. A handful of existing works…