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rogue-security/prompt-injections-benchmark

published by @rogue-security

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
18.3K
Last 30 days
1.1K
Likes
52
Size
5.5 MB
Created on HF
2025-01-28
Age
1 years ago

Description

Dataset: Qualifire Benchmark Prompt Injection(Jailbreak vs. Benign) Datasets

Overview

This dataset contains 5,000 prompts, each labeled as either jailbreak or benign. The dataset is designed for evaluating AI models' robustness against adversarial prompts and their ability to distinguish between safe and unsafe inputs.

Dataset Structure

Total Samples: 5,000

Labels: jailbreak, benign

Columns:

text: The input text

label: The classification… See the full description on the dataset page: https://huggingface.co/datasets/rogue-security/prompt-injections-benchmark.

Tags

license:cc-by-nc-4.0size_categories:1K<n<10Kformat:parquetformat:optimized-parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:uspromptinjectionjailbreakbenign

README current version from Hugging Face

license: cc-by-nc-4.0
tags:

  • prompt
  • injection
  • jailbreak
  • benign
    dataset_info:
    features:
    • name: text
      dtype: string
    • name: label
      dtype: string
      splits:
    • name: test
      num_bytes: 3637463
      num_examples: 5000
      download_size: 2109708
      dataset_size: 3637463
      configs:
  • config_name: default
    data_files:
    • split: test
      path: data/test-*

Dataset: Qualifire Benchmark Prompt Injection(Jailbreak vs. Benign) Datasets

Overview

This dataset contains 5,000 prompts, each labeled as either jailbreak or benign. The dataset is designed for evaluating AI models' robustness against adversarial prompts and their ability to distinguish between safe and unsafe inputs.

Dataset Structure

  • Total Samples: 5,000
  • Labels: jailbreak, benign
  • Columns:
    • text: The input text
    • label: The classification (jailbreak or benign)

License

cc-by-nc-4.0

README history 12 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.

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  2. 2025-02-02Update README.md32fbc6f626 B
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  3. 2025-02-02Update README.md34040d8957 B
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  7. 2025-02-02Create README.md0e760ab31 B
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  8. 2025-02-01Upload test.csv with huggingface_hub27a4c290 B
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  11. 2025-01-28Upload test.csv with huggingface_hub65302880 B
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  12. 2025-01-28initial commitee641da0 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

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Read further

Community best practices (evaluation)
4 rules on judges, benchmarks, and reporting.
Reference-set drift over time
Why the same benchmark means different things across model generations.
How we label the method for each model
The 12 classification rules that assign M1-M8 to every model.
Non-English abliteration communities
The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

License
cc-by-nc-4.0

Repository

DIR data FILE .gitattributes FILE README.md FILE test.csv

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

Download the dataset

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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("rogue-security/prompt-injections-benchmark")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="rogue-security/prompt-injections-benchmark", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/rogue-security/prompt-injections-benchmark
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