大型语言模型中近期后门攻击与防御综述
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models
AI总结：
本文综述面向大型语言模型的后门攻击，按全参数微调、参数高效微调和无微调三类系统梳理，并指出未来更隐蔽、免微调攻击等研究方向。
摘要：
大型语言模型（LLMs）弥合了人类语言理解与复杂问题解决之间的鸿沟，在多项 NLP 任务上取得了最先进性能，尤其是在少样本和零样本设置下。尽管 LLMs 已展现出显著成效，但由于计算资源限制，用户不得不使用开源语言模型，或将整个训练过程外包给第三方平台。然而，研究表明，语言模型容易受到潜在安全漏洞的影响，尤其是后门攻击。后门攻击旨在通过投毒训练样本或模型权重，向语言模型引入有针对性的漏洞，使攻击者能够通过恶意触发器操纵模型响应。虽然现有关于后门攻击的综述提供了全面概述，但它们缺乏对专门针对 LLMs 的后门攻击的深入考察。为弥补这一差距并把握该领域的最新趋势，本文通过聚焦微调方法，提出了关于 LLMs 后门攻击的新视角。具体而言，我们系统地将后门攻击分为三类：全参数微调、参数高效微调和无微调。基于大量综述所得的洞见，我们还讨论了未来后门攻击研究的关键问题，例如进一步探索不需要微调的攻击算法，或开发更隐蔽的攻击算法。
英文摘要：
Large Language Models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks, particularly in few-shot and zero-shot settings. Despite the demonstrable efficacy of LLMs, due to constraints on computational resources, users have to engage with open-source language models or outsource the entire training process to third-party platforms. However, research has demonstrated that language models are susceptible to potential security vulnerabilities, particularly in backdoor attacks. Backdoor attacks are designed to introduce targeted vulnerabilities into language models by poisoning training samples or model weights, allowing attackers to manipulate model responses through malicious triggers. While existing surveys on backdoor attacks provide a comprehensive overview, they lack an in-depth examination of backdoor attacks specifically targeting LLMs. To bridge this gap and grasp the latest trends in the field, this paper presents a novel perspective on backdoor attacks for LLMs by focusing on fine-tuning methods. Specifically, we systematically classify backdoor attacks into three categories: full-parameter fine-tuning, parameter-efficient fine-tuning, and no fine-tuning Based on insights from a substantial review, we also discuss crucial issues for future research on backdoor attacks, such as further exploring attack algorithms that do not require fine-tuning, or developing more covert attack algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
