README.md

metadata

language:
  - en
license: cc-by-4.0
pretty_name: JailbreakDB
task_categories:
  - text-classification
tags:
  - llm-safety
  - jailbreak
  - prompts
  - security
configs:
  - config_name: prompt_corpus
    default: true
    data_files:
      - split: jailbreak
        path: text_jailbreak_unique.csv
      - split: regular
        path: text_regular_unique.csv

JailbreakDB

JailbreakDB is a large-scale prompt corpus for LLM safety and prompt-security research. It contains two deduplicated, text-only CSV files:

• text_jailbreak_unique.csv (~6.6M rows): jailbreak and adversarial prompts.
• text_regular_unique.csv (~5.7M rows): benign prompts.

Each row stores the prompt text and lightweight source metadata. The associated PromptSecurity evaluation measurements are maintained as a separate Hugging Face dataset: https://huggingface.co/datasets/youbin2014/PromptSecurity-Eval.

Related resources:

• Code: https://github.com/datasec-lab/PromptSecurity
• Leaderboard: https://datasec-lab.github.io/PromptSecurityLeaderboard/

Data Files

Split	File	Description
jailbreak	text_jailbreak_unique.csv	Jailbreak and adversarial prompts.
regular	text_regular_unique.csv	Benign prompts.

CSV Field Reference

The prompt-corpus CSV files use the following fields:

Field	Meaning
system_prompt	Optional system-side instruction associated with the prompt.
user_prompt	User-side prompt text.
jailbreak	Binary prompt label; 1 denotes jailbreak/adversarial prompt and 0 denotes benign prompt.
source	Source corpus or collection from which the prompt was collected.
tactic	Prompt tactic or category when available.

Loading

from datasets import load_dataset

corpus = load_dataset("youbin2014/JailbreakDB", "prompt_corpus")
print(corpus)

The CSV files can also be loaded directly:

from datasets import load_dataset

files = {
    "jailbreak": "hf://datasets/youbin2014/JailbreakDB/text_jailbreak_unique.csv",
    "regular": "hf://datasets/youbin2014/JailbreakDB/text_regular_unique.csv",
}
corpus = load_dataset("csv", data_files=files)
print(corpus)

Paper

If you use JailbreakDB, please cite the PromptSecurity paper:

@misc{hong2025sokpromptsecurity,
  title = {SoK: Taxonomy and Evaluation of Prompt Security in Large Language Models},
  author = {Hong, Hanbin and Wu, Shuang and Feng, Shuya and Naderloui, Nima and Yan, Shenao and Zhang, Jingyu and Arastehfard, Ali and Huang, Heqing and Hong, Yuan},
  year = {2025},
  eprint = {2510.15476},
  archivePrefix = {arXiv},
  primaryClass = {cs.CR},
  doi = {10.48550/arXiv.2510.15476},
  url = {https://arxiv.org/abs/2510.15476}
}

Safety Notice

This dataset may contain harmful, offensive, or disturbing prompts. It is intended strictly for research on model safety, jailbreak robustness, defense evaluation, and prompt-security analysis. Please review your institutional and legal requirements before use.