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AudioJailbreak

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
Disinformation: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
Economic harm: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
Expert advice: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
Fraud/Deception: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confu
...
ict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
Privacy: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
Sexual/Adult content: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
  child 0, semantic_sim: double
  child 1, dict_confused: double
  child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
      child 0, harmfulness: double
      child 1, policy_violation: double
      child 2, relevance: double
  child 3, dict_dsr: double
gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
  child 0, harmfulness: double
  child 1, policy_violation: double
  child 2, relevance: double
dict_confused: double
semantic_sim: double
dict_dsr: double
to
{'dict_dsr': Value('float64'), 'semantic_sim': Value('float64'), 'dict_confused': Value('float64'), 'gpt_score': {'harmfulness': Value('float64'), 'policy_violation': Value('float64'), 'relevance': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Disinformation: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              Economic harm: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              Expert advice: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              Fraud/Deception: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confu
              ...
              ict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              Privacy: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              Sexual/Adult content: struct<semantic_sim: double, dict_confused: double, gpt_score: struct<harmfulness: double, policy_vi (... 54 chars omitted)
                child 0, semantic_sim: double
                child 1, dict_confused: double
                child 2, gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                    child 0, harmfulness: double
                    child 1, policy_violation: double
                    child 2, relevance: double
                child 3, dict_dsr: double
              gpt_score: struct<harmfulness: double, policy_violation: double, relevance: double>
                child 0, harmfulness: double
                child 1, policy_violation: double
                child 2, relevance: double
              dict_confused: double
              semantic_sim: double
              dict_dsr: double
              to
              {'dict_dsr': Value('float64'), 'semantic_sim': Value('float64'), 'dict_confused': Value('float64'), 'gpt_score': {'harmfulness': Value('float64'), 'policy_violation': Value('float64'), 'relevance': Value('float64')}}
              because column names don't match

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Check out the documentation for more information.

Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models

License: Apache 2.0 Python 3.10+ Dataset

AudioJailbreak is a benchmark framework specifically designed for evaluating the security of Audio Language Models (Audio LLMs). This project tests model defenses against malicious requests through various audio perturbation techniques.
Note: This project aims to improve the security of audio language models. Researchers should use this tool responsibly.

📋 Table of Contents

📝 Project Overview

AudioJailbreak provides a comprehensive evaluation framework for testing the robustness of audio language models against adversarial attacks. Our method incorporates carefully designed perturbations in audio inputs to test model security mechanisms. Key features include:

  • Diverse test cases: Covering multiple categories of harmful speech samples
  • Automated evaluation pipeline: End-to-end automation from audio processing to result analysis
  • Bayesian optimization: Intelligent search for optimal perturbation parameters
  • Multi-model compatibility: Support for evaluating mainstream audio language models

🔧 Installation Guide

  1. Clone repository:
git clone https://github.com/PbRQianJiang/AudioJailbreak.git
cd AudioJailbreak
  1. Create and activate environment:
conda env create -f environment.yaml
conda activate Audiojailbreak
  1. Download dataset (from Hugging Face):
Link: https://huggingface.co/datasets/NEUQ-LIS-LAB/AudioJailbreak

💾 Dataset

Important Notice: This repository contains code only. All audio data and preprocessed/inference result JSONL files are hosted on Hugging Face.

Dataset includes:

  • Original speech samples (audio/)
  • Input JSONL files (convert/question)
  • Model responses (inference/response)
  • Evaluation results (eval/xx), where xx is model name
  • Original texts (text/)

📁 Code Structure 

AudioJailbreak/
├── audio/            # Audio processing tools (actual audio files on Hugging Face)
├── convert/          # Data conversion & formatting (actual JSONL files on Hugging Face)
├── eval/             # Evaluation scripts & metrics
├── inference/        # Model inference & adversarial testing
├── script/           # Speech conversion scripts
├── experiment/       # Threshold experiment configurations
└── text/             # Text processing utilities

Core Components

  • new_add_noise.py: Implements multiple audio perturbation methods
  • APT_BO_diva.py: Bayesian optimization-based adversarial sample generation
  • xx.py: Model inference interfaces (xx represents model name)
  • eval.py: Evaluation metric calculations

🚀 Usage

1. Data Preparation 

Link: https://huggingface.co/datasets/NEUQ-LIS-LAB/AudioJailbreak

2. Basic Audio Jailbreaking 

# Generate adversarial samples with Bayesian optimization
python infer/xx.py  # Replace xx with model name

3. APT-based Bayesian Audio Jailbreaking 

# Generate adversarial samples with Bayesian optimization
python infer/APT_BO_diva.py

4. Model Evaluation 

# Single model evaluation
python eval_utils/eval.py

# Batch evaluation
bash eval_utils/auto.sh

5. Result Analysis 

# Generate evaluation report
python eval/all_average.py

📊 Evaluation Metrics

Our benchmark uses multiple metrics to assess model security:

  • dict_dsr: Model refusal rate (dict_dsr=1-asr)
  • Semantic Similarity: Semantic similarity of refusal responses
  • GPT Score: Includes sub-metrics:
    • Harmfulness: Degree of harmful content
    • Policy Violation: Policy violation rate
    • Relevance: Response relevance

📄 Citation

If you use AudioJailbreak in your research, please cite our paper:

📜 License

This project is licensed under Apache License 2.0.

Note: This project aims to improve the security of audio language models. Researchers must use this tool responsibly.
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