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

Studies of the risks facing AI models follow a well trodden path. However, acoustic models provide a unique challenge: utterances exist in both the physical and digital world. However, across the adversarial literature this challenge is either ignored with digital only evaluations, or tested within a single lab environment. This is a methodological and metrological shortcoming that undermines our understanding of risk. Through real-world testing, conceptual discussions, and a novel, high-throughput acoustic simulation framework, we demonstrate a new path for adversarial acoustics. This includes formalizing and operationalizing a novel Dual-Form Signal to Noise Ratio to decouple source stealth from victim attack efficacy, resolving a crucial limitation in current works. We support this with experiments that demonstrate that attackers with coarse room knowledge achieve relative Word Error Rate increases of between 26 and 71%, rising to 94.5% under an oracle upper bound. This work lays the groundwork for repeatable, verifiable research that embraces, rather than abstracts, the acoustic environment.