license: cc-by-4.0
language: [en, tr]
task_categories: [text-classification]
tags: [prompt-injection, jailbreak, llm-security, ai-security, red-teaming, turkish, owasp-llm-top10, security]
pretty_name: "Multilingual Prompt Injection (EN + TR)"
size_categories: [n<1K]
configs: [{config_name: default, data_files: prompt_injection.jsonl}]
Multilingual Prompt Injection
from datasets import load_dataset
ds = load_dataset("fevziegeyurtsevenler/multilingual-prompt-injection")
217 labeled prompt-injection & jailbreak techniques across 8 categories, ~half Turkish — because non-English attacks slip past English-only filters. Every row ships with its defense. Defensive dataset (canary placeholders {SECRET}/{URL}; no real harmful content).
Columns: category, language, technique, payload, target_behavior, owasp, atlas, defense, severity. Categories: direct/indirect injection, jailbreak-persona, system-prompt-leak, data-exfiltration, encoding, Turkish morphological, tool/agent abuse.
Schema
| column | meaning |
|---|---|
| technique | technique name |
| payload | example ({SECRET}/{URL} placeholders) |
| target_behavior | expected bad outcome |
| owasp, atlas, severity | mapping |
| defense | mitigation |
Related AltaySec resources
- 🕵️ uncloak — hidden prompt-injection scanner: GitHub · live demo
- 📚 Datasets: detection-rules · glossary · skills-in-the-wild · agent-skill-vulnerabilities
- 🌐 AltaySec · Açık Kaynak Lab
Citation
@misc{yurtsevenler2026multilingualpromptinjection,
title = {Multilingual Prompt Injection Dataset (EN+TR)},
author = {Yurtsevenler, Fevzi Ege},
year = {2026},
publisher = {AltaySec / Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/fevziegeyurtsevenler/multilingual-prompt-injection}}
}
⚠️ Defensive / authorized-use only. Multilingual-first LLM/agent security research by AltaySec.