Organizes audio jailbreaks into semantic/acoustic/signal/embedding categories, evaluates representative attacks and defenses on ten LALMs with success rate plus latency and benign refusal, and concludes that acoustic attacks are potent while defenses trade robustness for usability.
The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer
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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 safety vulnerabilities across modalities remains underexplored. This question is critical as text-based jailbreak attacks are considerably more mature than audio-based ones; if they transfer systematically, current audio safety evaluations may underestimate risks originating from the text modality. In this paper, we introduce the Alignment Curse, a formally characterized and empirically validated principle showing that stronger modality alignment enables more effective transfer of attacks from text to audio, revealing a fundamental tension between capability and safety. Motivated by this principle, we conduct a comprehensive black-box evaluation of three attack categories on recent omni-models (e.g., Qwen2.5-Omni, Qwen3-Omni): text attacks, text-transferred audio attacks, and audio attacks. We find that text-transferred audio attacks perform comparably to, and often better than, audio-based attacks, exhibiting a clear advantage under audio-only access. This suggests that text-based vulnerabilities play a pivotal role in shaping audio safety risks. Finally, we empirically analyze the relationship between modality alignment and transfer effectiveness across attack methods and models, observing consistent support for the Alignment Curse: tighter modality alignment leads to more effective cross-modality attack transfer.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
D²-Monitor routes between lightweight and heavy safety probes using the count of hesitation steps in diffusion LLM denoising trajectories, achieving SOTA trade-off on three datasets with under 0.85M parameters.
citing papers explorer
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Audio Jailbreaks in Large Audio-Language Models: Taxonomy, Attack-Defense Analysis, and Cost-Aware Evaluation
Organizes audio jailbreaks into semantic/acoustic/signal/embedding categories, evaluates representative attacks and defenses on ten LALMs with success rate plus latency and benign refusal, and concludes that acoustic attacks are potent while defenses trade robustness for usability.
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$D^2$-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing
D²-Monitor routes between lightweight and heavy safety probes using the count of hesitation steps in diffusion LLM denoising trajectories, achieving SOTA trade-off on three datasets with under 0.85M parameters.