ARENA: Automated Red-Teaming for Large Audio Language Models
Large audio-language models (LALMs) make it possible to interact with language models through speech, music, and environmental sound, but they also introduce a safety surface that is difficult to expose with text-only red-teaming. We study automated audio-grounded red-teaming, where a text query must remain safe in isolation while the joint text-audio input induces harmful target behavior. We propose ARENA, a closed-loop framework that trains a controller on an independent 2,000case text-audio dataset. MD-Judge supplies training rewards and adaptive search feedback, while a separate, non-adaptive Llama Guard 3 evaluator alone labels final outcomes. On 520 held-out AdvBench objectives, ARE...
| Authors | Jiaming He, Zhicong Huang, Tian Jin et al. |
|---|---|
| Primary category | cs.SD |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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