Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations
It addresses safety vulnerabilities in multimodal AI systems for real-world deployment, though it is incremental as it focuses on an underexplored aspect of existing models.
This paper investigated how speaker emotional variations affect the safety of large audio-language models, finding that different emotions and intensities lead to substantial inconsistencies in unsafe responses, with medium emotional expressions often posing the greatest risk.
Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker emotion. We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs. Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk. These findings highlight an overlooked vulnerability in LALMs and call for alignment strategies explicitly designed to ensure robustness under emotional variation, a prerequisite for trustworthy deployment in real-world settings.