标题： 针对语音语言模型的后门攻击 显示英文标题

标题： Backdoor Attacks Against Speech Language Models

摘要： 大型语言模型（LLMs）及其多模态扩展正变得越来越流行。 一种常见的实现多模态的方法是将特定领域的编码器与LLM级联，使最终模型继承其所有组件的漏洞。 在本工作中，我们对语音语言模型进行了首次系统的音频后门攻击研究。 我们在四种语音编码器和三个数据集上展示了其有效性，涵盖了四个任务：自动语音识别（ASR）、语音情感识别、性别和年龄预测。 该攻击始终能实现高成功率，范围从90.76%到99.41%。 为了更好地理解后门如何传播，我们进行了一项组件分析，以确定管道中最易受攻击的阶段。 最后，我们提出了一种基于微调的防御方法，以减轻受污染预训练编码器的威胁。 显示英文摘要

摘要： Large Language Models (LLMs) and their multimodal extensions are becoming increasingly popular. One common approach to enable multimodality is to cascade domain-specific encoders with an LLM, making the resulting model inherit vulnerabilities from all of its components. In this work, we present the first systematic study of audio backdoor attacks against speech language models. We demonstrate its effectiveness across four speech encoders and three datasets, covering four tasks: automatic speech recognition (ASR), speech emotion recognition, and gender and age prediction. The attack consistently achieves high success rates, ranging from 90.76% to 99.41%. To better understand how backdoors propagate, we conduct a component-wise analysis to identify the most vulnerable stages of the pipeline. Finally, we propose a fine-tuning-based defense that mitigates the threat of poisoned pretrained encoders.