  Recently, large language models (LLMs) have been gaining a lot of interest
due to their adaptability and extensibility in emerging applications, including
communication networks. It is anticipated that 6G mobile edge computing
networks will be able to support LLMs as a service, as they provide ultra
reliable low-latency communications and closed loop massive connectivity.
However, LLMs are vulnerable to data and model privacy issues that affect the
trustworthiness of LLMs to be deployed for user-based services. In this paper,
we explore the security vulnerabilities associated with fine-tuning LLMs in 6G
networks, in particular the membership inference attack. We define the
characteristics of an attack network that can perform a membership inference
attack if the attacker has access to the fine-tuned model for the downstream
task. We show that the membership inference attacks are effective for any
downstream task, which can lead to a personal data breach when using LLM as a
service. The experimental results show that the attack success rate of maximum
92% can be achieved on named entity recognition task. Based on the experimental
analysis, we discuss possible defense mechanisms and present possible research
directions to make the LLMs more trustworthy in the context of 6G networks.
