2024/08/01 by Sunder Ali Khowaja, Khowaja, Sunder Ali, Parus Khuwaja +7 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Blockchain Technology Applications and Security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Software-Defined Networks and 5G #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2408.00722
openalex publication_date 2024/08/01 · openalex created_date 2024/08/04 · openalex updated_date 2026/07/28
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 ZSM 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 ZSM 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 ZSM networks.