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Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks

arXiv Security Archived Jun 29, 2026 ✓ Full text saved

arXiv:2606.27701v1 Announce Type: cross Abstract: While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the difficulties in scaling strictly digital adversarial workflows to the physical world. These scale barriers have led the community to abstract away key acoustic factors relating to detectability and the influence of geometry on acoustics. These methodological and metrolog

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    Computer Science > Sound [Submitted on 26 Jun 2026] Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks Andrew C. Cullen, Neil Marchant, Jiani Xie, Paul Montague, Benjamin I.P. Rubinstein While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the difficulties in scaling strictly digital adversarial workflows to the physical world. These scale barriers have led the community to abstract away key acoustic factors relating to detectability and the influence of geometry on acoustics. These methodological and metrological shortcomings undermine our understanding of risk. We illuminate these issues through real-world testing, conceptual discussions, and a novel, high-throughput reality simulation framework. By testing over 8 million adversarial evaluations, we demonstrate that acoustic awareness yields relative Word Error Rate increases of up to 94.5\% under Whisper and wav2vec. We employ this framework to explore a formalize and operationalize a Dual-Form Signal to Noise Ratio to decouple source stealth from victim attack efficacy, resolving a crucial limitation in current works. This lays the groundwork for repeatable, verifiable research that embraces, rather than abstracts, the acoustic environment. Comments: 20 pages Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2606.27701 [cs.SD]   (or arXiv:2606.27701v1 [cs.SD] for this version)   https://doi.org/10.48550/arXiv.2606.27701 Focus to learn more Submission history From: Andrew Cullen [view email] [v1] Fri, 26 Jun 2026 04:00:01 UTC (1,017 KB) Access Paper: HTML (experimental) view license Current browse context: cs.SD < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI cs.CR cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 29, 2026
    Archived
    Jun 29, 2026
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