Pathway to Secure and Trustworthy ZSM for LLMs: Attacks, Defense, and Opportunities

TL;DR

This paper investigates security vulnerabilities, specifically membership inference attacks, on fine-tuned LLMs in ZSM networks, demonstrating high attack success rates and discussing defense strategies to enhance trustworthiness.

Contribution

It identifies and analyzes membership inference vulnerabilities in LLMs within ZSM networks and proposes directions for improving their security and trustworthiness.

Findings

Membership inference attacks can reach up to 92% success rate.

Attacks are effective across various downstream tasks.

Discussion of defense mechanisms and future research directions.

Abstract

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.