---
license: cc-by-4.0
language:
- ar
- en
tags:
- llm-security
- prompt-injection
- jailbreak-detection
- arabic-nlp
- triple-anchor
---

# SemGuard: Arabic Security Dataset (v2)

This dataset is released as part of the research paper: **"SemGuard: A Triple-Anchor Semantic Security Gateway for Multilingual Prompt Attack Detection in Large Language Models"**.

## Dataset Overview
The dataset provides a comprehensive benchmark for evaluating LLM prompt attacks in Arabic, Arabizi, and English across multiple threat categories.

### Files Included:
1. `semguard_arabic_security_v2_accepted.csv`: Clean, validated dataset (807 examples) ready for direct model training and evaluation.
2. `semguard_arabic_security_v2_accepted_detailed.csv`: Detailed version of the validated dataset including individual LLM judges' outputs and agreement scores.
3. `semguard_arabic_security_v2_rejected_disagreement.csv`: The Disagreement Corpus (527 examples) containing generated instances rejected by the LLM-as-a-Judge pipeline.
4. `semguard_arabic_security_v2_raw_generated.csv`: The complete raw set of generated instances (1,334 examples) prior to validation.

## Source Code
The source code will be made publicly available soon once the repository setup is fully archived. Stay tuned!

## Citation
If you use this dataset in your research, please cite our paper:

```bibtex
@dataset{abughallous2026semguard,
  author       = {Abdullah M. Abughallous},
  title        = {SemGuard: Arabic Security Dataset for Multilingual Prompt Attack Detection },
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/AG-31625874/SemGuard-Dataset},
  note         = {Multilingual Arabic, Arabizi, and English prompt attack detection benchmark}

```
# Paper Citation
```bibtex
@inproceedings{abughallous2026semguard_paper,
  author    = {Abughallous, Abdullah M. and Abufakher, Somia},
  title     = {SemGuard: A Triple-Anchor Semantic Security Gateway for Multilingual Prompt Attack Detection in Large Language Models},
  booktitle = {IEEE Jordan International Conference on Electrical Engineering and Information Technologies (AEECT)},
  year      = {2026},
  publisher = {IEEE}
}
```
}