Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks
cs.LG, cs.AI, cs.CR
Submitted: 2026-06-05
Updated: 2026-09-28
Code: https://github.com/jnxie/semantic-gambit
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on
Terminology
Abstract
Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on incomplete information. This causal constraint serves as an information bottleneck on attackers, significantly limiting attack performance. Our new Semantic Gambit attack breaks this causal limitation by augmenting the adversary with predictive context derived from a Large Language Model in real-time. Our experiments show that this form of augmentation can elevate the corpus-level Word Error Rate to 35.6% -- a three-fold increase over the current state-of-the-art. Ultimately, this work reveals how common, low-latency LLM tooling can be exploited to systematically subvert real-time ASR pipelines.
Sources
- Perceptual Based Adversarial Audio Attacks
- The Llama 3 Herd of Models
- Universal Adversarial Audio Perturbations
- GFLean: An Autoformalisation Framework for Lean via GF
- Jailbreaking Black Box Large Language Models in Twenty Queries
- Large Language Models are not Fair Evaluators
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