CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference

Autor:	Yu, Erxin, Li, Jing, Liao, Ming, Wang, Siqi, Gao, Zuchen, Mi, Fei, Hong, Lanqing
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	As large language models (LLMs) constantly evolve, ensuring their safety remains a critical research problem. Previous red-teaming approaches for LLM safety have primarily focused on single prompt attacks or goal hijacking. To the best of our knowledge, we are the first to study LLM safety in multi-turn dialogue coreference. We created a dataset of 1,400 questions across 14 categories, each featuring multi-turn coreference safety attacks. We then conducted detailed evaluations on five widely used open-source LLMs. The results indicated that under multi-turn coreference safety attacks, the highest attack success rate was 56% with the LLaMA2-Chat-7b model, while the lowest was 13.9% with the Mistral-7B-Instruct model. These findings highlight the safety vulnerabilities in LLMs during dialogue coreference interactions. Comment: Submitted to EMNLP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.17626 Zobrazit plný text záznamu View this record from Arxiv