arXiv:2509.19858v2 Announce Type: replace Abstract: As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input becomes critical. Although prior work has studied adversarial attacks in text-based LLMs and vision-language models, the unique cognitive and perceptual challenges of speech-based interaction remain underexplored. In contrast, speech presents inherent ambiguity, continuity, and perceptual diversity, which make adversarial attacks more difficult to detect. In this paper,
As Speech LLMs proliferate in voice-based applications, the need to identify and protect against sophisticated adversarial attacks that exploit speech's unique properties becomes urgent.
This study highlights critical vulnerabilities in Speech LLMs, suggesting that their increasing integration into sensitive applications could introduce new attack vectors if robustness measures are not adequately addressed.
The understanding of adversarial attacks expands beyond text and vision to specifically include the complex and underexplored challenges inherent in speech-based AI interactions.
- · AI Security Researchers
- · Cybersecurity Firms
- · Developers of robust Speech LLMs
- · Users relying on unhardened Speech LLMs
- · Voice-based application providers with weak security
- · Developers neglecting speech-specific attack vectors
Increased research and development into defensive mechanisms for Speech LLMs against 'gaslighting' and other adversarial attacks.
New industry standards and regulatory guidelines for the security and robustness of voice-based AI systems emerge.
Enhanced trust or widespread distrust in voice-controlled interfaces, depending on the industry's ability to mitigate these vulnerabilities effectively.
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Read at arXiv cs.CL