CHASE: Contextual History for Adaptive and Simple Exploitation in Large Language Model Jailbreaking
Zhiqiang Hao, Chuanyi Li, Ye Fan, Jun Cai, Xiao Fu, Shangqi Wang, Hao Shen, Jiao Yin
Abstract
We propose Contextual History for Adaptive and Simple Exploitation (CHASE), a novel multi-turn method for Large Language Model (LLM) jailbreaking. Rather than directly attack an LLM that may be difficult to jailbreak, CHASE first collects jailbroken histories from an easy-to-jailbreak LLM and then transfers them to the target LLM. Through this history transfer process, CHASE misleads the target LLM into thinking that it is responsible for producing the jailbroken histories and increases the chances of successful jailbreaking by prompting it to continue the conversation. Extensive evaluations on mainstream LLMs show that CHASE consistently achieves higher attack success rates and demands fewer computational resources compared to existing methods.
BibTeX
@inproceedings{aaai2026_chasecontextualh,
title = {CHASE: Contextual History for Adaptive and Simple Exploitation in Large Language Model Jailbreaking},
author = {Zhiqiang Hao and Chuanyi Li and Ye Fan and Jun Cai and Xiao Fu and Shangqi Wang and Hao Shen and Jiao Yin and Jidong Ge and Bin Luo and Vincent Ng},
booktitle = {AAAI 2026},
year = {2026}
}