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.

中文翻译：

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通信网络中大型语言模型中的后门攻击的全面概述


大型语言模型（LLMs ）凭借其在语言理解和生成方面的卓越能力，有望为未来的移动通信网络提供高效、智能的服务。然而，其性能对数据和计算资源的要求极高。LLMs迫使开发人员求助于外包培训或利用第三方数据和计算资源。这些策略可能会将网络内的模型暴露给恶意操纵的训练数据和处理，从而为攻击者提供了将隐藏的后门嵌入到模型中的机会，称为后门攻击。后门攻击LLMs指的是在其中嵌入隐藏的后门LLMs这导致模型在良性样本上表现正常，但在中毒样本上表现出性能下降。这个问题在可靠性和安全性至关重要的通信网络中尤其令人担忧。尽管对后门攻击进行了广泛的研究，但仍然缺乏专门针对后门攻击的深入探索LLMs用于通信网络，目前缺乏对此类攻击的系统审查。在本次调查中，我们系统地提出了后门攻击的分类法LLMs按照通信网络中的使用方式，将其分为四大类：输入触发、提示触发、指令触发和演示触发攻击。此外，我们对基准数据集进行了全面分析。最后，我们确定了潜在的问题和开放的挑战，为未来的研究方向提供了宝贵的见解，以增强数据的安全性和完整性。LLMs在通信网络中。