most citedUniversal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2025

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models

Jinming Wen, Xinyi Wu, Shuai Zhao +2

Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual ta…

cs.CR2024

Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning

Shuai Zhao, Leilei Gan, Luu Anh Tuan +4

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to language models have been proposed and successfully implemented. However, this raises the que…

cs.CL2024★ 2 cited

Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning

Shuai Zhao, Meihuizi Jia, Luu Anh Tuan +2

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite…