@inproceedings{
yong2023lowresource,
title={Low-Resource Languages Jailbreak {GPT}-4},
author={Zheng Xin Yong and Cristina Menghini and Stephen Bach},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=pn83r8V2sv}
}
AI safety training and red-teaming of large language models (LLMs) are measures to mitigate the generation of unsafe content. Our work exposes the inherent cross-lingual vulnerability of these safety mechanisms, resulting from the linguistic inequality of safety training data, by successfully circumventing GPT-4's safeguard through translating unsafe English inputs into low-resource languages. On the AdvBench benchmark, GPT-4 engages with the unsafe translated inputs and provides actionable items that can get the users towards their harmful goals 79% of the time, which is on par with or even surpassing state-of-the-art jailbreaking attacks. Other high-/mid-resource languages have significantly lower attack success rates, which suggests that the cross-lingual vulnerability mainly applies to low-resource languages. Previously, limited training on low-resource languages primarily affected speakers of those languages, causing technological disparities.  However, our work highlights a crucial shift: this deficiency now poses a risk to all LLMs users. Publicly available translation APIs enable anyone to exploit LLMs' safety vulnerabilities. Therefore, our work calls for more holistic red-teaming efforts to develop robust multilingual safeguards with wide language coverage.
Low-Resource Languages Jailbreak GPT-4
Zheng Xin Yong Cristina Menghini Stephen Bach