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license: mit
language:
- en
tags:
- cybersecurity
- vulnerability
- mitre-attck
- text-classification
- fine-tuned
base_model: ehsanaghaei/SecureBERT
---
# SecureBERT — MITRE ATT&CK Classifier
[](https://theses.fr/s371241)
[](https://opensource.org/licenses/MIT)
[](https://doi.org/10.5281/zenodo.16936476)
[](https://zenodo.org/records/17368476)
[](https://github.com/terranovafr/CVE-LMTune)
<div align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5b/Logo_Universit%C3%A9_de_Lorraine.svg/1280px-Logo_Universit%C3%A9_de_Lorraine.svg.png" alt="Universite de Lorraine" height="50"/>
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/95/Inr_logo_rouge.svg/1280px-Inr_logo_rouge.svg.png" alt="INRIA" height="50"/>
<img src="https://upload.wikimedia.org/wikipedia/fr/6/6e/Logo_loria_abrege_couleur.png" alt="LORIA" height="70"/>
<img src="https://www.pepr-cybersecurite.fr/wp-content/uploads/2023/09/pep-cybersecurite-550x250-1.png" alt="SuperViZ" height="70"/>
</div>
<br>
Part of the **CVE-LMTune** model suite — language models fine-tuned for multi-taxonomy vulnerability classification.
## Paper
> Franco Terranova, Sana Rekbi, Abdelkader Lahmadi, Isabelle Chrisment.
> *Multi-Taxonomy Vulnerability Classification with Hierarchically Finetuned Language Models.*
> The 23rd Conference on Detection of Intrusions and Malware & Vulnerability Assessment **(DIMVA '26)**.
## Task
**MITRE ATT&CK technique classification from CVE descriptions**
## Performance
See paper for details
## Model Structure
flat — standard `AutoModelForSequenceClassification`
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("Sana9/securebert-mitre-attack")
model = AutoModelForSequenceClassification.from_pretrained("Sana9/securebert-mitre-attack")
model.eval()
text = "Buffer overflow vulnerability in OpenSSL allows remote attackers to execute arbitrary code."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits) # multi-label → sigmoid
```
> **Note for hierarchical models:** This repo contains multiple sub-folders (master + slave models).
> Load each sub-folder separately using `from_pretrained("Sana9/securebert-mitre-attack/master")` etc.
## Citation
```bibtex
@inproceedings{terranova2026cvelmtune,
title = {Multi-Taxonomy Vulnerability Classification with Hierarchically Finetuned Language Models},
author = {Terranova, Franco and Rekbi, Sana and Lahmadi, Abdelkader and Chrisment, Isabelle},
booktitle = {Proceedings of DIMVA '26},
year = {2026}
}
```
## Related Resources
- 🤗 [Full model suite on Hugging Face](https://huggingface.co/Sana9)
- 💻 [CVE-LMTune — Training code (GitHub)](https://github.com/terranovafr/CVE-LMTune)
- 📦 [Zenodo — Data repository](https://doi.org/10.5281/zenodo.16936476)
- 📦 [Zenodo — Code repository](https://zenodo.org/records/17368476)
## Disclaimers
- This product uses the NVD API but is not endorsed or certified by the NVD.
- This project relies on data publicly available from the CWE, CAPEC, and MITRE ATT&CK projects.
- This work has been partially supported by the French National Research Agency under the France 2030 label (Superviz ANR-22-PECY-0008). The views reflected herein do not necessarily reflect the opinion of the French government.
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