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emgena/automated_redteaming_adversarial_jailbreak_eval_teaser

published by @emgena

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
116
Last 30 days
116
Likes
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Size
1.2 MB
Created on HF
2026-09-21
Age
1 months ago

Description

🚀 AI Safety - Adversarial Red-Teaming Jailbreak & Prompt Injection Benchmark (Evaluation Teaser)

⚡ Official Free Evaluation Teaser (50 Verified Multi-Turn Scenarios)🏆 Get the Full Production Package (500 Samples) & Commercial EULA on Gumroad:👉 Purchase Full Production Master Dataset on Gumroad🏷️ Use coupon code LAUNCH20 for 20 € off at checkout!

🌟 Domain Focus & Capabilities

Comprehensive red-teaming vectors, multi-lingual token smuggling probes, and… See the full description on the dataset page: https://huggingface.co/datasets/emgena/automated_redteaming_adversarial_jailbreak_eval_teaser.

Tags

task_categories:text-generationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usemgenasynthetic-dataagentic-aimulti-turnunslothautomated-redteaming-adversarial-jailbreak-eval

README current version from Hugging Face

language:

  • en
    license: apache-2.0
    task_categories:
  • conversational
  • text-generation
    tags:
  • emgena
  • synthetic-data
  • agentic-ai
  • multi-turn
  • unsloth
  • automated-redteaming-adversarial-jailbreak-eval
    pretty_name: "AI Safety - Adversarial Red-Teaming Jailbreak & Prompt Injection Benchmark (Evaluation Teaser)"
    size_categories:
  • n<1K

🚀 AI Safety - Adversarial Red-Teaming Jailbreak & Prompt Injection Benchmark (Evaluation Teaser)

⚡ Official Free Evaluation Teaser (50 Verified Multi-Turn Scenarios)
🏆 Get the Full Production Package (500 Samples) & Commercial EULA on Gumroad:
👉 Purchase Full Production Master Dataset on Gumroad
🏷️ Use coupon code LAUNCH20 for 20 € off at checkout!

🌟 Domain Focus & Capabilities

Comprehensive red-teaming vectors, multi-lingual token smuggling probes, and safety boundary evaluation datasets for enterprise LLMs.

  • FAANG Version 2.0 Architectural Standard
  • 100% AST Syntactic Validation (Zero Syntax Errors)
  • Multi-Turn Thought Reasoning Chain
  • 25% Self-Healing Failure Recovery Injections

📦 Full Package Highlights (Available on Gumroad):

  1. Full Production Parquet: 500 multi-turn scenarios with zero-crop architecture.
  2. 1-Click Google Colab Notebook: Fine-tune with Unsloth in <15 minutes on a free T4 GPU.
  3. AST Verifier Suite: Instant offline validation with verify_dataset.py.
  4. EU AI Act & EULA Compliance: Certified for enterprise deployment under Articles 50 & 53.

👉 Unlock Full Dataset & Commercial License on Gumroad

README history 5 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. 2026-10-01Upload benchmark_report.html with huggingface_hubc567c1f1.7 KB
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  2. 2026-10-01Upload EU_AI_ACT_ANNEX_IV_AUDIT.md with huggingface_hubd0de7a61.7 KB
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  3. 2026-10-01Upload Modelfile with huggingface_hub036523f1.7 KB
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  4. 2026-09-21Deploy official 50-sample evaluation teaser for automated_redteaming_adversar...3b45e911.7 KB
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  5. 2026-09-21initial commit647ecc10 B
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Community best practices (evaluation)
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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.
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The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

License
apache-2.0

Download the dataset

0

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