  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 AdvBenchmark, 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 rate, which suggests that the cross-lingual
vulnerability mainly applies to low-resource languages. Previously, limited
training on low-resource languages primarily affects 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 a more holistic red-teaming
efforts to develop robust multilingual safeguards with wide language coverage.
