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cve-severity-net
harinpurumandla/cve-severity-net
cve-severity-net is a machine learning model from harinpurumandla. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Fine-tuned google/flan-t5-base (250M parameters) on 200k NVD CVE description → CVSS v3.1 base vector pairs. Predicts all 8 base metrics from free-text vulnerability descriptions.
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At a glance
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Jun 8, 2026
- Updated
- Jun 8, 2026
- SHA
- 5cb470d9
Model card
From the Hugging Face model README
cve-severity-net
Fine-tuned google/flan-t5-base (250M parameters) on 200k NVD CVE description → CVSS v3.1
base vector pairs. Predicts all 8 base metrics from free-text vulnerability descriptions.
Trained on CVEs from 2016–2023. Tested on a held-out 2025 split (temporal, never seen during training).
Evaluation (2025 test set, n=40,179)
| Metric | Score |
|---|---|
| Exact vector match | 44.9% |
| Severity band accuracy | 67.4% |
| Score MAE | 0.91 |
Per-component accuracy
| AV | AC | PR | UI | S | C | I | A |
|---|---|---|---|---|---|---|---|
| 93.9% | 91.6% | 75.8% | 92.5% | 91.6% | 81.3% | 81.8% | 80.7% |
PR (Privileges Required) and C/I/A (Impact) are the hardest components — advisory text rarely encodes privilege context or impact scope explicitly.
Baseline comparison (1000-CVE subsample, seed=42)
| Model | Exact Match | Band Accuracy | MAE | Cost/1k CVEs |
|---|---|---|---|---|
| Majority class (always "High") | 0.0% | ~45% | ~1.8 | $0 |
| FLAN-T5-base fine-tuned (this model) | 45.9% | 66.9% | 0.93 | $0 |
| Claude Opus 4.8 (zero-shot) | 34.5% | 62.6% | 0.97 | $2.07 |
| Claude Opus 4.8 (few-shot) | 33.0% | 60.0% | 1.08 | $4.29 |
| GPT-5.4 (zero-shot) | 26.5% | 57.8% | 1.23 | $0.68 |
| GPT-5.4 (few-shot) | 30.5% | 55.9% | 1.25 | $1.30 |
| Gemma4 E4B local (zero-shot) | 12.9% | 38.0% | 1.93 | $0 |
| Gemma4 E4B local (few-shot) | 14.3% | 37.7% | 1.89 | $0 |
Limitations
- English descriptions only — non-English CVEs are excluded during training and prediction
- CVSS v3.1 base vector only — no temporal, environmental, or threat metrics
- 44.9% exact match means ~55% of predictions have at least one wrong component — do not use as a sole authoritative source for security scoring decisions
- Novel attack patterns not represented in 2016–2023 training data may degrade accuracy
- Few-shot prompting of frontier LLMs did not improve their performance on this task
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("harinpurumandla/cve-severity-net")
model = AutoModelForSeq2SeqLM.from_pretrained("harinpurumandla/cve-severity-net")
description = "A heap-based buffer overflow allows unauthenticated remote code execution."
inputs = tokenizer(
"predict cvss vector: " + description,
return_tensors="pt",
truncation=True,
max_length=512,
)
outputs = model.generate(**inputs, num_beams=4, max_new_tokens=48)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Training details
- Base model:
google/flan-t5-base - Dataset: 200k NVD CVE description → CVSS v3.1 vector pairs (2016–2023)
- Epochs: 5, best checkpoint at epoch 4 (val exact vector match: 49.2%)
- Hardware: single NVIDIA RTX 3090, fp16
- Input format:
"predict cvss vector: {description}"
- Type
- t5
- License
- apache-2.0
- Languages
- en
- Created
- Jun 8, 2026
- Updated
- Jun 8, 2026