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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: title |
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dtype: string |
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- name: description |
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dtype: string |
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- name: cpes |
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sequence: string |
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- name: cvss_v4_0 |
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dtype: float64 |
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- name: cvss_v3_1 |
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dtype: float64 |
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- name: cvss_v3_0 |
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dtype: float64 |
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- name: cvss_v2_0 |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 363023583.0092845 |
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num_examples: 559803 |
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- name: test |
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num_bytes: 40336385.990715496 |
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num_examples: 62201 |
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download_size: 158862200 |
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dataset_size: 403359969 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- text-classification |
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license: cc-by-4.0 |
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library_name: datasets |
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tags: |
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- vulnerability |
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- cybersecurity |
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- security |
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- cve |
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- cvss |
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--- |
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This dataset, `CIRCL/vulnerability-scores`, comprises over 600,000 real-world vulnerabilities used to train and evaluate VLAI, a transformer-based model designed to predict software vulnerability severity levels directly from text descriptions, enabling faster and more consistent triage. |
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The dataset is presented in the paper [VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification](https://huggingface.co/papers/2507.03607). |
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Project page: [https://vulnerability.circl.lu](https://vulnerability.circl.lu) |
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Associated code: [https://github.com/vulnerability-lookup/ML-Gateway](https://github.com/vulnerability-lookup/ML-Gateway) |
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### Sources of the data |
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- CVE Program (enriched with data from vulnrichment and Fraunhofer FKIE) |
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- GitHub Security Advisories |
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- PySec advisories |
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- CSAF Red Hat |
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- CSAF Cisco |
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- CSAF CISA |
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Extracted from the database of [Vulnerability-Lookup](https://vulnerability.circl.lu). |
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Dumps of the data are available [here](https://vulnerability.circl.lu/dumps/). |
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### Query with datasets |
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```python |
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import json |
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from datasets import load_dataset |
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dataset = load_dataset("CIRCL/vulnerability-scores") |
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vulnerabilities = ["CVE-2012-2339", "RHSA-2023:5964", "GHSA-7chm-34j8-4f22", "PYSEC-2024-225"] |
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filtered_entries = dataset.filter(lambda elem: elem["id"] in vulnerabilities) |
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for entry in filtered_entries["train"]: |
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print(json.dumps(entry, indent=4)) |
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``` |
