mark-matviiv/ukrainian-safety-dataset

Ukrainian LLM Safety Dataset

Dataset Summary

A national security-focused safety classification dataset for Ukrainian LLM infrastructure, developed in partnership with the Ministry of Digital Transformation of Ukraine. Contains adversarial prompts across a 9-class threat taxonomy with synthetic rationales and contrastive examples for knowledge distillation.

Files

File	Samples	Purpose
train.jsonl	59,377	Training corpus with contrastive triplets and rationales (synthetic + anonymized ministry logs)
benchmark.jsonl	450	Held-out adversarial benchmark (50 samples/class, human-verified)

Taxonomy (9 classes)

• cybercrime_hacking
• disinfo_propaganda
• fraud_soc_eng
• llm_compromise
• nat_sec_opsec
• privacy_pii
• resource_abuse
• safe
• unsafe_content

Format

train.jsonl

Each line is a JSON object with contrastive distillation fields:

{
  "prompt": "User prompt text (the adversarial query)",
  "tag": "cybercrime_hacking",
  "reasoning": "Detailed explanation of why this prompt belongs to the category, including manipulation techniques identified",
  "positive": "Semantically equivalent paraphrase preserving the same safety label",
  "negative": "A semantically close but safe alternative on the same topic (hard negative)",
  "model_name": "Teacher model used for augmentation"
}

Fields:

• prompt (string): The original adversarial or benign user query
• tag (string): Safety category label from the 9-class taxonomy
• reasoning (string): Teacher-generated natural-language rationale explaining the classification logic
• positive (string): Paraphrased variant of prompt preserving the same tag (used for contrastive learning)
• negative (string): A safe query on the same topic with high lexical overlap but different intent (hard negative for ACD)
• model_name (string): Identifier of the teacher model that synthesized this sample

benchmark.jsonl

Each line is a JSON object with minimal fields for evaluation:

{
  "prompt": "User prompt text",
  "tag": "llm_compromise"
}

Fields:

• prompt (string): The adversarial query
• tag (string): Ground-truth safety category label

Usage

from datasets import load_dataset

dataset = load_dataset("mark-matviiv/ukrainian-safety-dataset")
train = dataset["train"]
benchmark = dataset["benchmark"]

Access

This dataset is gated. Access requests are reviewed by the Ministry of Digital Transformation of Ukraine.

Citation

@mastersthesis{matviiv2026guardrail,
  title={Efficient Guardrailing for the Ukrainian LLM via Reasoning Distillation},
  author={Matviiv, Markiian},
  year={2026},
  school={Ukrainian Catholic University}
}