5 papers
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
Han Liu, Zhi Xu, Xiaotong Zhang +5
Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that…
HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
Han Liu, Jiaqi Li, Zhi Xu +5
Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be considered simultaneously, and on…
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Han Liu, Haotian Gao, Xiaotong Zhang +5
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective t…
RUQuant: Towards Refining Uniform Quantization for Large Language Models
Han Liu, Haotian Gao, Changya Li +4
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-traini…
TAO-Attack: Toward Advanced Optimization-Based Jailbreak Attacks for Large Language Models
Zhi Xu, Jiaqi Li, Xiaotong Zhang +2
Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers craft prompts that bypass safe…