When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR

Abstract:SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice: users supply context in prompts with sensitive information, fine-tune on proprietary recordings, or both. We identify and systematically investigate an overlooked privacy risk of such customisation: a model adapted to recognise domain-specific terminology can be nudged into transcribing a phonetically similar word from its context or training data, even when a different word is spoken, thereby leaking private information. To evaluate this risk, we propose a technique to automatically construct benchmarks of such attacks and apply it to measure leakage rates across two customisation mechanisms, prompting and fine-tuning. Both mechanisms cause measurable leakage, compounding when combined. We evaluate a prompt-level mitigation strategy and analyse the accuracy-leakage trade-off across customisation approaches, finding that fine-tuning without context prompts offers the best balance.

Computation and Language (cs.CL)

arXiv:2605.28211 [cs.CL]

(or arXiv:2605.28211v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.28211 Focus to learn more arXiv-issued DOI via DataCite

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