Papers by Kongcheng Zhang
MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models (2025.emnlp-main)
Copied to clipboard
Siyu Yan, Long Zeng, Xuecheng Wu, Chengcheng Han, Kongcheng Zhang, Chong Peng, Xuezhi Cao, Xunliang Cai, Chenjuan Guo
| Challenge: | Existing defenses target single-turn attacks, but real-world usage involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. |
| Approach: | They propose a framework that tackles multi-turn jailbreaks from both attack and defense angles. |
| Outcome: | Experiments on large language models show that MUSE effectively mitigates multi-turn jailbreaks. |