license: apache-2.0
language: [en, tr]
task_categories: [text-classification]
tags: [prompt-injection, jailbreak, llm-security, ai-security, detection, guardrails, red-teaming]
pretty_name: "Prompt Injection Detection Rules (EN+TR)"
size_categories: [n<1K]
configs: [{config_name: rules, data_files: rules.jsonl}]
Prompt Injection Detection Rules (EN + TR)
from datasets import load_dataset
ds = load_dataset("fevziegeyurtsevenler/prompt-injection-detection-rules")
Regex detection rules for prompt injection & jailbreak — drop into a guardrail, WAF or log pipeline.
Schema
| column | meaning |
|---|---|
| id | PID-CAT-NNN |
| category, severity, lang | grouping / risk / language |
| pattern | regex |
| maps_to, false_positive, description | OWASP/ATLAS, FP note, detail |
Related AltaySec resources
- 🕵️ uncloak scanner: https://github.com/fevziegeyurtsevenler/uncloak · live demo
- 📚 Other datasets: detection-rules · glossary · skills-in-the-wild
- 🌐 AltaySec · Açık Kaynak Lab
Citation
@misc{yurtsevenler2026promptinjectiondetectionrules,
title = {Prompt Injection Detection Rules},
author = {Yurtsevenler, Fevzi Ege},
year = {2026},
publisher = {AltaySec / Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/fevziegeyurtsevenler/prompt-injection-detection-rules}}
}
Multilingual-first LLM/agent security research by AltaySec.