As diverse linguistic communities and users adopt large language models (LLMs), assessing their safety across languages becomes critical. Despite ongoing efforts to make LLMs safe, they can still be made to behave unsafely with jailbreaking, a technique in which models are prompted to act outside their operational guidelines. Research on LLM safety and jailbreaking, however, has so far mostly focused on English, limiting our understanding of LLM safety in other languages. We contribute towards closing this gap by investigating the effectiveness of many-shot jailbreaking, where models are prompted with unsafe demonstrations to induce unsafe behaviour, in Italian. To enable our analysis, we create a new dataset of unsafe Italian question-answer pairs. With this dataset, we identify clear safety vulnerabilities in four families of open-weight LLMs. We find that the models exhibit unsafe behaviors even when prompted with few unsafe demonstrations, and -- more alarmingly -- that this tendency rapidly escalates with more demonstrations.

本研究针对大型语言模型（LLM）在非英语环境下的安全性漏洞进行了调查，填补了当前研究的空白。我们通过构建一个包含不安全意大利问答对的新数据集，发现意大利LLM在多次越狱提示下表现出明显的不安全行为，尤其是在少量不安全示范的情况下，这种不安全倾向会迅速加剧。

妥协！意大利多重越狱攻击削弱大型语言模型的安全性