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New research finds modality alignment transfers AI audio attacks

A new research paper introduces the "Alignment Curse," a principle demonstrating how improved text-audio modality alignment in omni-models can inadvertently transfer safety vulnerabilities from text to audio. Researchers found that text-transferred audio attacks are as effective as, and often superior to, audio-only attacks, suggesting current audio safety evaluations may underestimate risks. The study analyzed models like Qwen2.5-Omni and Qwen3-Omni, finding a consistent correlation between tighter modality alignment and more effective cross-modality attack transfer. AI

IMPACT Highlights a fundamental tension between AI capability and safety, suggesting current audio safety measures may be insufficient.

RANK_REASON Research paper introducing a new principle and empirical findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yupeng Chen, Junchi Yu, Aoxi Liu, Baoyuan Wu, Philip Torr, Adel Bibi ·

    The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer

    arXiv:2602.02557v2 Announce Type: replace-cross Abstract: Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignment. However, whether such alignment inadvertently facilitates the transfer of saf…