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When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research identifies privacy risks in speech LLMs where domain customisation prompts lead models to leak sensitive context vocabulary.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 24 September 2026
- Collected by OneBench
- 25 Sept 2026, 03:01 UK
- Original headline
- When Helpful Context Leaks: Privacy Risks in Domain-Adapted ASR ↗
Stored source excerpt
arXiv:2605.28211v2 Announce Type: replace Abstract: SpeechLLMs are increasingly deployed in professional settings where domain customisation is standard practice: users supply context in prompts with sensitive…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Domain customisation in speech models introduces data leakage vectors that challenge existing enterprise privacy controls.
Do what
Review data leakage risks with the team evaluating speech-to-text models for customer service applications.