Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large
  Audio-Language Models
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.
682f0439adae717195c35dd3
Zirui Song
682f0439adae717195c35dd4
Qian Jiang
682f0439adae717195c35dd5
Mingxuan Cui
682f0439adae717195c35dd6
Mingzhe Li
682f0439adae717195c35dd7
Lang Gao
682f0439adae717195c35dd8
Zeyu Zhang
682f0439adae717195c35dd9
Zixiang Xu
682f0439adae717195c35dda
Yanbo Wang
682f0439adae717195c35ddb
Chenxi Wang
682f0439adae717195c35ddc
Guangxian Ouyang
682f0439adae717195c35ddd
Zhenhao Chen
682f0439adae717195c35dde
Xiuying Chen
Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large
  Audio-Language Models
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.
AJailBench evaluates jailbreak vulnerabilities in Large Audio Language Models using a dataset of adversarial audio prompts, revealing consistent safety weaknesses and the effectiveness of semantically preserved perturbations.
Large Audio Language Models jailbreak attacks AJailBench AJailBench-Base adversarial audio prompts policy-violating categories text to speech synthesis state-of-the-art LAMs dynamic adversarial variants Audio Perturbation Toolkit APT targeted distortions time domain frequency domain amplitude domain semantic consistency constraint Bayesian optimization AJailBench-APT