license, datasets, language, base_model, tags
| license | datasets | language | base_model | tags | ||||||
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| apache-2.0 |
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QCRI/AZERG-T1-Mistral
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 specialized for Task 1: STIX Entity Detection. It has been trained on the QCRI/AZERG-Dataset to identify CTI-related entities (e.g., malware names, threat actors, vulnerabilities) in unstructured text.
This is a specialist model designed for high performance on entity detection within the AZERG framework.
Intended Use
Use this model to extract potential STIX entities from a given security text passage.
Example Prompt:
Instruction:
You are a helpful threat intelligence analyst. Your task is to extract all STIX entities mentioned in the input. To help you, here is a list of the possible STIX entity types.
STIX entity types:
- ATTACK_PATTERN: A type of TTP that describes ways that adversaries attempt to compromise targets. (e.g., T1051, T1548.001, etc.)
[...]
Answer in the following format: <entities>LIST OF IDENTIFIED ENTITIES SEPARATED BY PIPE |</entities>
Input:
- Text Passage: [INPUT TEXT]
Response:
Citation
If you use this model, please cite our paper:
@article{lekssays2025azerg,
title={From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction},
author={Lekssays, Ahmed and Sencar, Husrev Taha and Yu, Ting},
journal={arXiv preprint arXiv:2507.16576},
year={2025}
}