Large Language Models (LLMs) are poised to revolutionize 6G networks, offering personalized, AI-driven services. But what about security? This research delves into a critical vulnerability: membership inference attacks. Imagine an attacker trying to figure out if your personal data was used to train an LLM powering a 6G service. This research explores exactly that scenario. It turns out, LLMs can inadvertently leak information about their training data when fine-tuned for specific tasks. The researchers tested popular LLMs like RoBERTa and ALBERT, using datasets like Yelp Review and CoNLL2003. The results are concerning, with attack success rates reaching as high as 92%. This means sensitive data could be at risk. The paper proposes defenses like active learning and curriculum learning to reduce the amount of data needed for fine-tuning, making LLMs less prone to memorization and leakage. They also suggest implementing a "trust evaluation module" within the 6G edge layer to evaluate the trustworthiness of deployed LLMs based on different criteria, thus adding a layer of security. The integration of LLMs and 6G presents exciting opportunities but also critical security challenges. Ensuring user privacy and responsible AI usage needs urgent attention as we move towards a future of personalized, AI-powered services in 6G.