library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vulnerability-severity-classification-roberta-base
results: []
datasets:
- CIRCL/vulnerability-scores
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
Severity classification
This model is a fine-tuned version of roberta-base on the dataset CIRCL/vulnerability-scores.
The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv].
Abstract: VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.
You can read this page for more information.
Model description
It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.
It achieves the following results on the evaluation set:
- Loss: 0.5052
- Accuracy: 0.8289
How to get started with the model
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
labels = ["low", "medium", "high", "critical"]
model_name = "CIRCL/vulnerability-severity-classification-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()
test_description = "SAP NetWeaver Visual Composer Metadata Uploader is not protected with a proper authorization, allowing unauthenticated agent to upload potentially malicious executable binaries \
that could severely harm the host system. This could significantly affect the confidentiality, integrity, and availability of the targeted system."
inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Print results
print("Predictions:", predictions)
predicted_class = torch.argmax(predictions, dim=-1).item()
print("Predicted severity:", labels[predicted_class])
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7443 | 1.0 | 28795 | 0.6486 | 0.7343 |
| 0.6199 | 2.0 | 57590 | 0.5670 | 0.7743 |
| 0.4911 | 3.0 | 86385 | 0.5249 | 0.7958 |
| 0.3388 | 4.0 | 115180 | 0.4890 | 0.8185 |
| 0.4451 | 5.0 | 143975 | 0.5052 | 0.8289 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4