While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem. Although several preventive measures have been developed to mitigate the potential risks associated with LLMs, they have primarily focused on English data. In this study, we reveal the presence of multilingual jailbreak challenges within LLMs and consider two potential risky scenarios: unintentional and intentional. The unintentional scenario involves users querying LLMs using non-English prompts and inadvertently bypassing the safety mechanisms, while the intentional scenario entails malicious users combining jailbreak instructions with multilingual prompts to attack LLMs deliberately. The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases. Specifically, low-resource languages exhibit three times the likelihood of encountering harmful content compared to high-resource languages, with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts can exacerbate the negative impact of jailbreak instructions, with astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for GPT-4. Finally, we propose a novel \textsc{Self-Defense} framework that addresses the  multilingual jailbreak challenges  via automatically generating multilingual safety training data for fine-tuning. Experiment results demonstrate its effectiveness with notable reduction in unsafe rate.
Multilingual Jailbreak Challenges in Large Language Models
Yue Deng Wenxuan Zhang Sinno Jialin Pan Lidong Bing