Datasets:

mark-matviiv
/

ukrainian-safety-dataset

license: other
license_name: restricted-research-license
license_link: LICENSE
language:
  - uk
  - en
  - ru
pretty_name: Ukrainian LLM Safety Dataset
size_categories:
  - 10K<n<100K
tags:
  - text-classification
  - safety
  - guardrail
  - adversarial
  - ukrainian
  - multilingual
  - llm

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}
}