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lvogel123/m2s-cosafe

published by @lvogel123

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
152
Last 30 days
15
Likes
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Size
204 KB
Created on HF
2025-10-06
Age
1 years ago

Description

M2S → CoSafe (single-turn)

This dataset contains single-turn prompts produced by applying the M2S conversion (“Hyphenize / Numberize / Pythonize”) to multi-turn jailbreak dialogues referenced in the M2S paper. We retain only minimal fields (IDs + prompt text); no model outputs or labels are included.

Intended use

Research on jailbreak robustness and defenses; please handle responsibly (adversarial/harmful prompts). We recommend enabling gated access.… See the full description on the dataset page: https://huggingface.co/datasets/lvogel123/m2s-cosafe.

Tags

task_categories:text-generationlanguage:ensize_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:usjailbreaksafetyadversarial-robustness

README current version from Hugging Face

pretty_name: M2S → CoSafe (single-turn)
language: [en]
tags: [jailbreak, safety, adversarial-robustness]
task_categories: [text-generation]

M2S → CoSafe (single-turn)

This dataset contains single-turn prompts produced by applying the M2S conversion (“Hyphenize / Numberize / Pythonize”) to multi-turn jailbreak dialogues referenced in the M2S paper. We retain only minimal fields (IDs + prompt text); no model outputs or labels are included.

Intended use

Research on jailbreak robustness and defenses; please handle responsibly (adversarial/harmful prompts). We recommend enabling gated access.

Citation

Please cite the M2S paper:

Junwoo Ha, Hyunjun Kim, Sangyoon Yu, Haon Park, Ashkan Yousefpour, Yuna Park, Suhyun Kim.
One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs. ACL 2025. (M2S)

Provenance & modifications

  • Source: multi-turn datasets as referenced in M2S; converted to single-turn via the M2S procedure.
  • Modifications: formatting to single-turn; dropped upstream responses/scores; de-duplication and light cleaning only.

README history 3 revisions

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. 2025-10-06Update README.md71aa9891.1 KB
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  2. 2025-10-06Upload dataset228d737577 B
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  3. 2025-10-06initial commit5a53fdb0 B
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Used by abliterated models none yet

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Repository

DIR data 3 files FILE .gitattributes FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

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("lvogel123/m2s-cosafe")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="lvogel123/m2s-cosafe", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/lvogel123/m2s-cosafe
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