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Casey27/JailbreakPrompts

published by @Casey27

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2026-03-30
Age
6 months ago

Description

Independent Jailbreak Datasets for LLM Guardrail Evaluation

Constructed for the thesis:“Contamination Effects: How Training Data Leakage Affects Red Team Evaluation of LLM Jailbreak Detection”

The effectiveness of LLM guardrails is commonly evaluated using open-source red teaming tools. However, this study reveals that significant data contamination exists between the training sets of binary jailbreak classifiers (ProtectAI, Katanemo, TestSavantAI, etc.) and the test prompts… See the full description on the dataset page: https://huggingface.co/datasets/Casey27/JailbreakPrompts.

Tags

language:enlicense:mitsize_categories:1K<n<10Kformat:csvmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usJailbreakPromptInjectionRedTeamingJailbreakingPromptsChatGPTJailbreakingLLM

README current version from Hugging Face


license: mit
language:

  • en
    tags:
  • Jailbreak
  • PromptInjection
  • RedTeaming
  • JailbreakingPrompts
  • ChatGPT
  • Jailbreaking
  • LLM
    pretty_name: Jailbreaking prompts
    configs:
  • config_name: default
    data_files:
    • split: Dataset_1
      path: data/Dataset1/*
    • split: Dataset_2
      path: data/Dataset2/*

Independent Jailbreak Datasets for LLM Guardrail Evaluation

Constructed for the thesis:
“Contamination Effects: How Training Data Leakage Affects Red Team Evaluation of LLM Jailbreak Detection”

The effectiveness of LLM guardrails is commonly evaluated using open-source red teaming tools. However, this study reveals that significant data contamination exists between the training sets of binary jailbreak classifiers (ProtectAI, Katanemo, TestSavantAI, etc.) and the test prompts used in state-of-the-art red teaming tools (Garak, PyRIT, Giskard, etc.). On average, over 65% of red team evaluation prompts were also present in the training data of the tested classifiers. This contamination can lead to significantly inflated performance metrics for these jailbreak classifiers and other LLM guardrails.

To address this, we present two datasets consisting of independent jailbreak prompts curated to enable contamination-free evaluation of binary jailbreak classifiers and other LLM guardrails. All prompts have been filtered to ensure no overlap with the training data of widely used jailbreak classifiers. This allows for a more accurate assessment of a classifier’s generalization capabilities.

Dataset Overview

  • Dataset 1 – Filtered High-Quality Prompts
    A manually selected and filtered set of structurally diverse and high-quality jailbreak prompts.

  • Dataset 2 – Broad Coverage Prompts
    A larger, more inclusive, non-filtered dataset constructed to mitigate selection bias.


Prompt Sources


README history 1 revision

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-03-30Duplicate from Simsonsun/JailbreakPrompts35804742.7 KB
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Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-03-31[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
mit

Repository

Download the dataset

Gated dataset. You need to accept the author's terms on Hugging Face before downloading. Visit the repository, click Agree and access repository, then log in locally with huggingface-cli login and use one of the snippets below.

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("Casey27/JailbreakPrompts")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Casey27/JailbreakPrompts", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/Casey27/JailbreakPrompts
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