---
license: mit
language: en
tags: [cvss, cve, vulnerability, security, text-classification]
datasets: [nvd]
base_model: microsoft/deberta-v3-base
---

# CVEscore: CVSS v3.1 vector prediction from CVE descriptions

Fine-tuned `microsoft/deberta-v3-base` with 8 classification heads (AV, AC, PR, UI, S, C, I, A).
The base score and severity are computed from the predicted vector with the official CVSS v3.1 formula.

## Input format
Prefix the description with who will score the CVE, as in training:

- `[NVD] <description>` predicts the vector NVD would give.
- `[CNA: <org>] <description>` predicts the vector that CNA would give. `<org>` is the domain of the CNA's
  email without the TLD (`security@wordfence.com` -> `wordfence`), or `cisa-adp` for CISA's ADP.

Remove CVSS vectors and scores quoted in the description first (`strip_cvss` in `src/cvss_text.py`):
the model was trained without them.

With every CVE prompted as `[NVD]`, exact match is 0.393 overall (0.552 on NVD-scored CVEs).
Tagging each CVE with its real scorer raises CNA-scored exact match from 0.339 to 0.438.
Ablation: https://github.com/atomic-crafter/CVEscore/blob/master/results/ablation.md

## Training data and split
NVD CVE JSON 2.0 feeds, CVEs with a CVSS v3.1 vector (NVD score preferred, CNA score otherwise).
**Time-based split**: train on CVEs published before 2024-10-01, validate up to the end of 2024,
test on CVEs published from 2025 onwards. No random split, so no leakage from the future.

## Test results (123,459 CVEs published in 2025+, each tagged with its scorer)
| Metric | Value |
|---|---|
| Exact vector match | 0.467 |
| Severity accuracy | 0.691 |
| Base score MAE | 0.860 |
| Inference speed | 1.01 ms/CVE (RTX 5080, batched) |

Per-metric macro F1: AV 0.782, AC 0.707, PR 0.746, UI 0.903, S 0.851, C 0.819, I 0.819, A 0.796

## Intended use
Triage aid: pre-score new CVEs before official scoring is available, and route low-confidence
predictions (product of the 8 heads' top probabilities) to a human analyst.
Not a replacement for analyst scoring.

## Limitations
- English descriptions only; vague descriptions ("unspecified vulnerability") are scored poorly.
- NVD and vendor (CNA) scores disagree on most CVEs scored by both (18% exact agreement in 2025+),
  which caps achievable accuracy.
- CVSS v3.1 only (no v4.0).

## Usage
Code, training pipeline and the `CVSSModel` class: https://github.com/atomic-crafter/CVEscore

```python
from model import CVSSModel, predict_proba   # src/model.py in the GitHub repo
model, tokenizer, config = CVSSModel.load("path/to/this/repo", device="cuda")
```