  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.
