license: mit
task_categories:

• text-generation
• question-answering
  pretty_name: JailBreakV_28K
  size_categories:
• 10K<n<100K
  configs:
• config_name: JailBreakV_28K
  data_files:
  • split: mini_JailBreakV_28K
    path: JailBreakV_28K/mini_JailBreakV_28K.csv
  • split: JailBreakV_28K
    path: JailBreakV_28K/JailBreakV_28K.csv
• config_name: RedTeam_2K
  data_files:
  • split: RedTeam_2K
    path: JailBreakV_28K/RedTeam_2K.csv

⛓‍💥 JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks

🌐 GitHub | 🛎 Project Page ｜ 👉 Download full datasets

If you like our project, please give us a star ⭐ on Hugging Face for the latest update.

📰 News

📥 Using our dataset via huggingface Dataset

from datasets import load_dataset


mini_JailBreakV_28K = load_dataset("JailbreakV-28K/JailBreakV-28k", 'JailBreakV_28K')["mini_JailBreakV_28K"]
JailBreakV_28K = load_dataset("JailbreakV-28K/JailBreakV-28k", 'JailBreakV_28K')["JailBreakV_28K"]
RedTeam_2K = load_dataset("JailbreakV-28K/JailBreakV-28k", 'RedTeam_2K')["RedTeam_2K"]

👻 Inference and Evaluation

Create environment

conda create -n jbv python=3.9
conda activate jbv
pip install -r requirements.txt

Conduct jailbreak attack on MLLMs

# we default use Bunny-v1_0, you can change the default attack model to your customized attack models by editing the annotated codes.
# You can follow the Bunny script in <attack_models> to add other attack models.
python attack.py --root JailBreakV_28K 

Conduct evaluation

# we default use LlamaGuard, you can change the default evaluate model to your customized evaluate models by editing the annotated codes.
# You can follow the LlamaGuard script in <evaluate_models> to add other evaluate models.
python eval.py --data_path ./results/JailBreakV_28k/<your customized attack model>/JailBreakV_28K.csv

😃 Dataset Details

JailBreakV_28K and mini_JailBreakV_28K datasets will comprise the following columns:

• id: Unique identifier for all samples.
• jailbreak_query: Jailbreak_query obtained by different jailbreak attacks.
• redteam_query: Harmful query from RedTeam_2K.
• format: Jailbreak attack method including template, persuade, logic, figstep, query-relevant.
• policy: The safety policy that redteam_query against.
• image_path: The file path of the image.
• from: The source of data.
• selected_mini: "True" if the data in mini_JailBreakV_28K dataset, otherwise "False".
• transfer_from_llm: "True" if the jailbreak_query is transferred from LLM jailbreak attacks, otherwise "False".

RedTeam_2K will comprise the following columns:

• id: Unique identifier for all samples.
• question: Harmful query.
• policy: the safety policy that redteam_query against.
• from: The source of data.

🚀 Data Composition

RedTeam-2K: RedTeam-2K dataset, a meticulously curated collection of 2, 000 harmful queries aimed at identifying alignment vulnerabilities within LLMs and MLLMs. This dataset spans across 16 safety policies and incorporates queries from 8 distinct sources.

JailBreakV-28K: JailBreakV-28K contains 28, 000 jailbreak text-image pairs, which include 20, 000 text-based LLM transfer jailbreak attacks and 8, 000 image-based MLLM jailbreak attacks. This dataset covers 16 safety policies and 5 diverse jailbreak methods.

🛠️ Dataset Overview

The RedTeam-2K dataset, is a meticulously curated collection of 2, 000 harmful queries aimed at identifying alignment vulnerabilities within LLMs and MLLMs. This dataset spans 16 safety policies and incorporates queries from 8 distinct sources, including GPT Rewrite, Handcraft, GPT Generate, LLM Jailbreak Study, AdvBench, BeaverTails, Question Set, and hh-rlhf of Anthropic.
Building upon the harmful query dataset provided by RedTeam-2K, JailBreakV-28K is designed as a comprehensive and diversified benchmark for evaluating the transferability of jailbreak attacks from LLMs to MLLMs, as well as assessing the alignment robustness of MLLMs against such attacks. Specifically, JailBreakV-28K contains 28, 000 jailbreak text-image pairs, which include 20, 000 text-based LLM transfer jailbreak attacks and 8, 000 image-based MLLM jailbreak attacks. This dataset covers 16 safety policies and 5 diverse jailbreak methods. The jailbreak methods are formed by 3 types of LLM transfer attacks that include Logic (Cognitive Overload), Persuade (Persuasive Adversarial Prompts), and Template (including both of Greedy Coordinate Gradient and handcrafted strategies), and 2 types of MLLM attacks including FigStep and Query-relevant attack. The JailBreakV-28K offers a broad spectrum of attack methodologies and integrates various image types like Nature, Random Noise, Typography, Stable Diffusion (SD), Blank, and SD+Typography Images. We believe JailBreakV-28K can serve as a comprehensive jailbreak benchmark for MLLMs.

🏆 Mini-Leaderboard

❌ Disclaimers

This dataset contains offensive content that may be disturbing, This benchmark is provided for educational and research purposes only.

📲 Contact

• Weidi Luo: [email protected]
• Siyuan Ma: [email protected]
• Xiaogeng Liu: [email protected]
• Chaowei Xiao: [email protected]

📖 BibTeX:

@misc{luo2024jailbreakv28k,
      title={JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks}, 
      author={Weidi Luo and Siyuan Ma and Xiaogeng Liu and Xiaoyu Guo and Chaowei Xiao},
      year={2024},
      eprint={2404.03027},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

[More Information Needed]