@inproceedings{sadasivan-etal-2026-attackers,
    title = "Attacker{'}s Noise Can Manipulate Your Audio-based {LLM} in the Real World",
    author = "Sadasivan, Vinu Sankar  and
      Feizi, Soheil  and
      Mathews, Rajiv  and
      Wang, Lun",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.66/",
    pages = "1430--1440",
    ISBN = "979-8-89176-380-7",
    abstract = "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 transferability of the attack and potential defensive measures."
}