Membership Inference Attacks against Large Audio Language Models

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• Common speech datasets exhibit near-perfect train/test separability (AUC ≈ 1.0) even without model inference, due to acoustic artifacts.result 95%
  Evidencepartial

  common speech datasets exhibit near-perfect train/test separability (AUC approximately 1.0) even without model inference

  Implicationpartial

  Directly stated in the abstract and supported by a table footnote indicating 'Near-perfect baseline AUC indicates trivial train/test separability'.

  Verificationpartial

  partial

• Standard MIA scores strongly correlate (correlation > 0.7) with blind acoustic artifacts in audio datasets.result 90%
  Evidencepartial

  the standard MIA scores strongly correlate with these blind acoustic artifacts (correlation greater than 0.7)

  Implicationpartial

  Explicitly stated in the abstract with a numeric correlation value.

  Verificationpartial

  partial

• Distribution-matched datasets enable reliable MIA evaluation without distribution shift confounds.method 85%
  Evidencepartial

  distribution-matched datasets enable reliable MIA evaluation without distribution shift confounds

  Implicationpartial

  Directly stated in the abstract as a key finding, with supporting results showing MIA performance collapses to near-random on such datasets.

  Verificationpartial

  partial

• LALM memorization is cross-modal, arising only from binding a speaker's vocal identity with its text.result 90%
  Evidencepartial

  LALM memorization is cross-modal, arising only from binding a speaker's vocal identity with its text

  Implicationpartial

  Explicitly stated in the abstract and analysis as a primary conclusion from modality disentanglement experiments.

  Verificationpartial

  partial

• The high capacity of LALMs enables membership leakage through memorized cross-modal mappings between acoustic signals and text.technical 85%
  Evidencepartial

  the high capacity of LALMs still enables membership leakage through memorized cross-modal mappings between acoustic signals and text

  Implicationpartial

  Directly stated in the analysis as a key mechanism for privacy risk, supported by the study's experimental framework.

  Verificationpartial

  partial

• A multi-modal blind baseline based on textual, spectral, and prosodic features can diagnose spurious MIA success driven by data artifacts.method 90%
  Evidencepartial

  Using a multi-modal blind baseline based on textual, spectral, and prosodic features, we demonstrate that common speech datasets exhibit near-perfect train/test separability

  Implicationpartial

  Explicitly described in the abstract and analysis as a core methodological contribution, with results demonstrating its diagnostic utility.

  Verificationpartial

  partial

• On distribution-matched datasets where blind baseline AUC ≈ 0.5, MIA performance collapses to near-random (AUC 50.7–52.4).result 90%
  Evidencepartial

  Under these rigorously controlled conditions, MIA performance collapses to near-random (AUC 50.7–52.4)

  Implicationpartial

  Explicitly stated in the results section with specific numeric AUC ranges provided.

  Verificationpartial

  partial

• Privacy leakage in LALMs reveals speaker–content associations, constituting a biometric privacy risk more granular and severe than textual duplication.limitation 80%
  Evidencepartial

  Such leakage reveals speaker–content associations, constituting a biometric privacy risk that is more granular and severe than textual duplication

  Implicationpartial

  Directly stated in the analysis as a key implication, though the severity claim is comparative and not directly quantified.

  Verificationpartial

  partial

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