Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks

Yue Zhou, Henry Peng Zou, Barbara Di Eugenio, Yang Zhang

Abstract
We find that language models have difficulties generating fallacious and deceptive reasoning. When asked to generate deceptive outputs, language models tend to leak honest counterparts but believe them to be false. Exploiting this deficiency, we propose a jailbreak attack method that elicits an aligned language model for malicious output. Specifically, we query the model to generate a fallacious yet deceptively real procedure for the harmful behavior. Since a fallacious procedure is generally considered fake and thus harmless by LLMs, it helps bypass the safeguard mechanism. Yet the output is factually harmful since the LLM cannot fabricate fallacious solutions but proposes truthful ones. We evaluate our approach over five safety-aligned large language models, comparing four previous jailbreak methods, and show that our approach achieves competitive performance with more harmful outputs. We believe the findings could be extended beyond model safety, such as self-verification and hallucination.
Anthology ID:
2024.emnlp-main.738
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13293–13304
Language:
URL:
https://aclanthology.org/2024.emnlp-main.738
DOI:
10.18653/v1/2024.emnlp-main.738
Bibkey:
Cite (ACL):
Yue Zhou, Henry Peng Zou, Barbara Di Eugenio, and Yang Zhang. 2024. Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13293–13304, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks (Zhou et al., EMNLP 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.738.pdf
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