Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913932694781952 |
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| author | Sadasivan, Vinu Sankar Feizi, Soheil Mathews, Rajiv Wang, Lun |
| author_facet | Sadasivan, Vinu Sankar Feizi, Soheil Mathews, Rajiv Wang, Lun |
| contents | This paper investigates the real-world vulnerabilities of audio-based large language models (ALLMs), such as Qwen2-Audio. We first demonstrate that an adversary can craft stealthy audio perturbations to manipulate ALLMs into exhibiting specific targeted behaviors, such as eliciting responses to wake-keywords (e.g., "Hey Qwen"), or triggering harmful behaviors (e.g. "Change my calendar event"). Subsequently, we show that playing adversarial background noise during user interaction with the ALLMs can significantly degrade the response quality. Crucially, our research illustrates the scalability of these attacks to real-world scenarios, impacting other innocent users when these adversarial noises are played through the air. Further, we discuss the transferrability of the attack, and potential defensive measures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World Sadasivan, Vinu Sankar Feizi, Soheil Mathews, Rajiv Wang, Lun Cryptography and Security Artificial Intelligence Sound Audio and Speech Processing This paper investigates the real-world vulnerabilities of audio-based large language models (ALLMs), such as Qwen2-Audio. We first demonstrate that an adversary can craft stealthy audio perturbations to manipulate ALLMs into exhibiting specific targeted behaviors, such as eliciting responses to wake-keywords (e.g., "Hey Qwen"), or triggering harmful behaviors (e.g. "Change my calendar event"). Subsequently, we show that playing adversarial background noise during user interaction with the ALLMs can significantly degrade the response quality. Crucially, our research illustrates the scalability of these attacks to real-world scenarios, impacting other innocent users when these adversarial noises are played through the air. Further, we discuss the transferrability of the attack, and potential defensive measures. |
| title | Attacker's Noise Can Manipulate Your Audio-based LLM in the Real World |
| topic | Cryptography and Security Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.06256 |