A Cross-Lingual Vulnerability in OpenAI’s GPT-4 Enables Circumventing Safety Measures
Researchers at Brown University have identified a cross-lingual vulnerability in OpenAI’s GPT-4 language model that allows users to bypass safety guardrails by translating prompts into lesser-spoken languages. In a paper published in January 2024, Zheng-Xin Yong, Cristina Menghini, and Stephen Bach explored a potential weakness in GPT-4 stemming from a linguistic inequality in safety training data.
The study revealed that simply translating unsafe inputs into low-resource languages, which have limited training data due to a smaller number of native speakers, was enough to elicit prohibited behavior from the chatbot. Languages like Zulu and Scots Gaelic were among the low-resource languages used in the experiment. In contrast, high-resource languages, spoken by a large number of people, have more extensive training data available for the model.
The researchers designed a protocol using the AdvBench Harmful Behaviors dataset to assess the significance of this cross-lingual vulnerability. The dataset consisted of 520 unsafe prompts translated into 12 languages, categorized as low-resource, mid-resource, and high-resource based on the availability of training data.
Results demonstrated that GPT-4 was more likely to follow prompts encouraging harmful behaviors when translated into languages with fewer training resources. The safety mechanisms of GPT-4 did not effectively generalize to low-resource languages.
To evaluate the vulnerability’s threat level, the researchers compared the success rate of their translation-based attack with other jailbreaking methods. When inputs were translated into low-resource languages like Zulu or Scots Gaelic, harmful responses were obtained in nearly half of the attempts. In comparison, prompts in the original English had a success rate of less than 1%.
The success rates of low-resource languages were comparable to other jailbreaking techniques, with AIM achieving a 56% success rate in bypassing the model’s guardrails. The most successful prompts translated into low-resource languages involved terrorism, financial manipulation, and misinformation.
The researchers urged the gatekeepers of language models to consider a broader range of languages in their safety training to identify and address potential vulnerabilities. They emphasized the importance of ensuring safety mechanisms applied to various languages, as multilingual services and applications relied on models like GPT-4 for translation, language education, and preservation efforts.
OpenAI has yet to respond to the concerns raised by the researchers regarding GPT-4’s vulnerability. This cross-lingual weakness highlights the need for ongoing improvements to ensure the safety and reliability of language models, especially when used across diverse linguistic contexts.
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