A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

TL;DR

This paper provides a systematic review and taxonomy of backdoor attacks in large language models used in communication networks, highlighting challenges and future research directions.

Contribution

It introduces a comprehensive taxonomy of backdoor attack types in LLMs within communication networks and analyzes benchmark datasets for these attacks.

Findings

Proposes four categories of backdoor attacks: input-triggered, prompt-triggered, instruction-triggered, demonstration-triggered.

Analyzes benchmark datasets for backdoor attack detection and mitigation.

Identifies open challenges and future research directions in securing LLMs in communication networks.

Abstract

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and computational resource requirements for the performance of LLMs compel developers to resort to outsourcing training or utilizing third-party data and computing resources. These strategies may expose the model within the network to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model, termed a backdoor attack. Backdoor attack in LLMs refers to embedding a hidden backdoor in LLMs that causes the model to perform normally on benign samples but exhibit degraded performance on poisoned ones. This issue is particularly concerning within communication networks where reliability and security are paramount. Despite the extensive research on backdoor attacks, there remains a lack of in-depth exploration specifically within the context of LLMs employed in communication networks, and a systematic review of such attacks is currently absent. In this survey, we systematically propose a taxonomy of backdoor attacks in LLMs as used in communication networks, dividing them into four major categories: input-triggered, prompt-triggered, instruction-triggered, and demonstration-triggered attacks. Furthermore, we conduct a comprehensive analysis of the benchmark datasets. Finally, we identify potential problems and open challenges, offering valuable insights into future research directions for enhancing the security and integrity of LLMs in communication networks.