<ul data-eligibleForWebStory="false"><li>The paper discusses challenges in machine learning safety introduced by multimodal large language models, with a focus on Audio-Language Models (ALMs).</li><li>It explores audio jailbreaks targeting ALMs, showing the first universal jailbreaks in the audio modality that can bypass alignment mechanisms and remain effective in simulated real-world conditions.</li><li>The research reveals that adversarial perturbations encode imperceptible toxic speech, suggesting that embedding linguistic features within audio signals can elicit toxic outputs.</li><li>The study highlights the importance of understanding interactions between modalities in multimodal models and provides insights to improve defenses against adversarial audio attacks.</li></ul>

"I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models

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