Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large
  Audio-Language Models
Authors: name: Zirui Song, name: Qian Jiang, name: Mingxuan Cui, name: Mingzhe Li, name: Lang Gao, name: Zeyu Zhang, name: Zixiang Xu, name: Yanbo Wang, name: Chenxi Wang, name: Guangxian Ouyang, name: Zhenhao Chen, name: Xiuying Chen

The rise of Large Audio Language Models (LAMs) brings both potential and
risks, as their audio outputs may contain harmful or unethical content.
However, current research lacks a systematic, quantitative evaluation of LAM
safety especially against jailbreak attacks, which are challenging due to the
temporal and semantic nature of speech. To bridge this gap, we introduce
AJailBench, the first benchmark specifically designed to evaluate jailbreak
vulnerabilities in LAMs. We begin by constructing AJailBench-Base, a dataset of
1,495 adversarial audio prompts spanning 10 policy-violating categories,
converted from textual jailbreak attacks using realistic text to speech
synthesis. Using this dataset, we evaluate several state-of-the-art LAMs and
reveal that none exhibit consistent robustness across attacks. To further
strengthen jailbreak testing and simulate more realistic attack conditions, we
propose a method to generate dynamic adversarial variants. Our Audio
Perturbation Toolkit (APT) applies targeted distortions across time, frequency,
and amplitude domains. To preserve the original jailbreak intent, we enforce a
semantic consistency constraint and employ Bayesian optimization to efficiently
search for perturbations that are both subtle and highly effective. This
results in AJailBench-APT, an extended dataset of optimized adversarial audio
samples. Our findings demonstrate that even small, semantically preserved
perturbations can significantly reduce the safety performance of leading LAMs,
underscoring the need for more robust and semantically aware defense
mechanisms.