  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.
