Recently, social networks have become the primary means of communication for many people, leading computational linguistics researchers to focus on the language used on these platforms. As online interactions grow, recognizing and preventing offensive messages targeting various groups has become urgent. However, finding a balance between detecting hate speech and preserving free expression while promoting inclusive language is challenging. Previous studies have highlighted the risks of automated analysis misinterpreting context, which can lead to the censorship of marginalized groups. Our study is the first to explore the reappropriative use of slurs in Italian by leveraging Large Language Models (LLMs) witha zero-shot approach. We revised annotations of an existing Italian homotransphobic dataset, developed new guidelines, and designed various prompts to address the LLMs task. Our findings illustrate the difficulty of this challenge and provide preliminary results on using LLMs for such a language specific task.