JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models

Understand

Large Audio Language Models (LALMs) have made significant progress.

• While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks that bypass safety alignment.
• However, there remains a lack of an adversarial audio dataset and a unified framework specifically designed to evaluate and compare jailbreak attacks against them.
• To address this gap, we introduce JALMBench, a comprehensive benchmark that assesses LALM safety against jailbreak attacks, comprising 11,316 text samples and 245,355 audio samples (>1,000 hours).