vuln-cat

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vuln-cat

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5132
• Accuracy: 0.9034

Model description

vuln-cat is a classification model based on fine-tuning of scibert. It categorizes CVE summaries into 11 types of vulnerabilities, with class labels including:

[
    'csrf',
    'directory_traversal',
    'file_inclusion', 
    'input_validation', 
    'memory_corruption', 
    'open_redirect', 
    'overflow', 
    'sql_injection', 
    'ssrf',
    'xss', 
    'xxe'
]

Usage

from transformers import pipeline

text = 'A path traversal exists in a specific dll of Trend Micro Mobile Security (Enterprise) 9.8 SP5 which could allow an authenticated remote attacker to delete arbitrary files.'

classifier = pipeline(
    "text-classification",
    model="conflick0/vuln-cat",
    padding=True,
    truncation=True,
    max_length=512,
)

classifier(text)
# [{'label': 'directory_traversal', 'score': 0.9969494938850403}]

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2