Attack Prompt Generation for Red Teaming and Defending Large Language Models

Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He

Abstract
Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks. Extensive experiments on different LLMs validate the effectiveness of our proposed attack and defense frameworks. Additionally, we release a series of attack prompts datasets named SAP with varying sizes, facilitating the safety evaluation and enhancement of more LLMs.
Anthology ID:
2023.findings-emnlp.143
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2176–2189
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.143
DOI:
10.18653/v1/2023.findings-emnlp.143
Bibkey:
Cite (ACL):
Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, and Xiangnan He. 2023. Attack Prompt Generation for Red Teaming and Defending Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2176–2189, Singapore. Association for Computational Linguistics.
Cite (Informal):
Attack Prompt Generation for Red Teaming and Defending Large Language Models (Deng et al., Findings 2023)
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https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.143.pdf
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