aiqbench analysis
This paper shows that a short, reusable audio prefix can steer speech recognition output in real time without knowing what the speaker will say or aligning to their audio. Reported success rates are high in digital tests (about 93-98%) and remain substantial over the air (about 90%), and the authors show the errors carry into voice assistants and AI note-taking tools. For readers building or deploying speech systems, the takeaway is that universal, input-agnostic audio attacks are a practical threat model, so ASR pipelines need robustness testing beyond per-utterance adversarial examples.
GhostPrefix: Synchronization-Free Universal Prefix Attacks for ASR Manipulation
arXiv:2610.42026v1 Announce Type: new Abstract: We present GhostPrefix, the first synchronization-free universal adversarial prefix attack against automatic speech recognition (ASR) systems. Unlike prior audio attacks, which are typically crafted per utterance, require synchronization with the victim waveform, or target narrow objectives, GhostPrefix learns a short, reusable audio segment prepended to arbitrary live speech to exert real-time control over ASR outputs without knowledge of the future utterance or temporal alignment. A key challenge is maintaining stable adversarial effects across unknown, evolving speech contexts. GhostPrefix combines token-level losses with a novel representation-level objective for stable, input-agnostic effects. GhostPrefix supports three attack modes: (i) prompted prefix attacks for semantic steering, (ii) targeted prefix attacks for transcript hijacking, and (iii) untargeted prefix attacks for transcription degradation. To remain perceptually inconspicuous, we regularize prefixes toward benign environmental-sound distributions and improve robustness against over-the-air distortions. We evaluate GhostPrefix across 13 open-source ASR models from four families. In digital settings, it achieves average attack success rates of 97.6% for prompted steering and 93.4% for targeted hijacking, while driving word error rate to 1.18 under untargeted attacks. Over the air, it achieves success rates of 91.5% and 89.5% for prompted and targeted modes, respectively. These manipulations propagate into downstream AI-assisted pipelines, including voice assistants and AI note-taking tools, causing systematic semantic misinterpretations. GhostPrefix exposes synchronization-free universal prefixes as a practical, under-explored threat to real-world ASR and downstream applications. Artifacts and demos are available at https://ghostprefix.github.io/.
Abstract:We present GhostPrefix, the first synchronization-free universal adversarial prefix attack against automatic speech recognition (ASR) systems. Unlike prior audio attacks, which are typically crafted per utterance, require synchronization with the victim waveform, or target narrow objectives, GhostPrefix learns a short, reusable audio segment prepended to arbitrary live speech to exert real-time control over ASR outputs without knowledge of the future utterance or temporal alignment. A key challenge is maintaining stable adversarial effects across unknown, evolving speech contexts. GhostPrefix combines token-level losses with a novel representation-level objective for stable, input-agnostic effects. GhostPrefix supports three attack modes: (i) prompted prefix attacks for semantic steering, (ii) targeted prefix attacks for transcript hijacking, and (iii) untargeted prefix attacks for transcription degradation. To remain perceptually inconspicuous, we regularize prefixes toward benign environmental-sound distributions and improve robustness against over-the-air distortions. We evaluate GhostPrefix across 13 open-source ASR models from four families. In digital settings, it achieves average attack success rates of 97.6% for prompted steering and 93.4% for targeted hijacking, while driving word error rate to 1.18 under untargeted attacks. Over the air, it achieves success rates of 91.5% and 89.5% for prompted and targeted modes, respectively. These manipulations propagate into downstream AI-assisted pipelines, including voice assistants and AI note-taking tools, causing systematic semantic misinterpretations. GhostPrefix exposes synchronization-free universal prefixes as a practical, under-explored threat to real-world ASR and downstream applications. Artifacts and demos are available at this https URL.
Machine Learning (cs.LG)
arXiv:2610.42026 [cs.LG]
(or arXiv:2610.42026v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.42026 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 328 words