Hijacking Large Language Models via Adversarial In-Context Learning

16 November 2023

Papers citing "Hijacking Large Language Models via Adversarial In-Context Learning"

8 / 8 papers shown

Title
Diversity Helps Jailbreak Large Language Models Weiliang Zhao Daniel Ben-Levi Wei Hao Junfeng Yang Chengzhi Mao AAML 43 0 0 06 Nov 2024
COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability Xing-ming Guo Fangxu Yu Huan Zhang Lianhui Qin Bin Hu AAML 109 69 0 13 Feb 2024
Hijacking Context in Large Multi-modal Models Joonhyun Jeong MLLM 20 3 0 07 Dec 2023
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks Erfan Shayegani Md Abdullah Al Mamun Yu Fu Pedram Zaree Yue Dong Nael B. Abu-Ghazaleh AAML 135 139 0 16 Oct 2023
Defending against Insertion-based Textual Backdoor Attacks via Attribution Jiazhao Li Zhuofeng Wu Wei Ping Chaowei Xiao V. Vydiswaran 34 13 0 03 May 2023
On the Relation between Sensitivity and Accuracy in In-context Learning Yanda Chen Chen Zhao Zhou Yu Kathleen McKeown He He 178 77 0 16 Sep 2022
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity Yao Lu Max Bartolo Alastair Moore Sebastian Riedel Pontus Stenetorp AILaw LRM 271 882 0 18 Apr 2021
What Makes Good In-Context Examples for GPT- $3$ ? Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin Weizhu Chen AAML RALM 275 1,296 0 17 Jan 2021