arXiv:2603.28378v2 Announce Type: replace-cross Abstract: We present the first systematic Membership Inference Attack (MIA) evaluation of LALMs. Using Multi-modal Blind Baselines based on textual, spectral and prosodic features, we demonstrate that common audio datasets exhibit near-perfect train/test separability (AUC ~ 1.0) even without model inference, thus MIA may primarily detect distribution shift. We therefore introduce a blind-baseline protocol to control for this confound. Under this protocol, we identify that the distribution-matched datasets enable reliable MIA evaluation without di
As AI models, particularly Large Audio Language Models (LALMs), become more prevalent and integrated, their security vulnerabilities and privacy implications are being systematically explored.
This research reveals new vectors for privacy compromise in advanced AI systems, highlighting a critical area for development in AI ethics and security, especially concerning sensitive personal data embedded in audio.
The understanding of LALM vulnerabilities shifts from theoretical to practically demonstrated, necessitating more robust privacy-preserving training protocols and evaluation methodologies.
- · AI ethicists
- · Cybersecurity firms specializing in AI
- · Developers of privacy-preserving AI frameworks
- · Regulatory bodies focusing on data privacy
- · Developers of LALMs with weak privacy controls
- · Users whose audio data is compromised
- · Organizations handling sensitive audio data without proper safeguards
Increased focus on privacy-aware model training and stricter data governance for audio AI.
Development of industry standards and regulatory frameworks for mitigating privacy risks in large language models.
A potential chilling effect on the adoption of certain LALM applications in sensitive domains without proven privacy guarantees.
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Read at arXiv cs.AI