arXiv Artificial Intelligence

ARENA: Automated Red-Teaming for Large Audio Language Models

1 hour ago 18.08.2026 07:00 👁 1 views
ARENA: Automated Red-Teaming for Large Audio Language Models

Quick summary

arXiv:2608.15578v1 Announce Type: cross Abstract: 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 ada

Key takeaways

  • arXiv:2608.15578v1 Announce Type: cross Abstract: 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.

Why it matters

“ARENA: Automated Red-Teaming for Large Audio Language Models” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗