ukcli/All-CVE-Records-Training-Dataset

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- cybersecurity
- cve
- vulnerability
size_categories:
- 100K<n<1M
---

# CVE Chat‑Style Multi‑Turn Cybersecurity Dataset (1999 – 2025)

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## 1. Project Overview

This repository hosts the **largest publicly available chat‑style, multi‑turn cybersecurity dataset to date**, containing **≈ 300 000 Common Vulnerabilities and Exposures (CVE) records** published between **1999 and 2025**. Each record has been meticulously parsed, enriched, and converted into a conversational format that is ideal for training and evaluating AI and AI‑Agent systems focused on vulnerability analysis, threat intelligence, and cyber‑defense automation.

## 2. Key Highlights. Key Highlights

| Feature | Description |
| ------------------- | --------------------------------------------------------------------------------------- |
| Records | \~300 k CVE entries (1999‑2025) |
| Formats Covered | CVE 4.0 (legacy) & CVE 5.0+ (modern) |
| Parsing Accuracy | **100 %** (validated) |
| Enrichments | CVSS v2 & v3 metrics · CWE taxonomy · Affected‑product matrices · Expert system prompts |
| Conversation Depth | Multi‑turn (System / User / Assistant) |
| Processing Pipeline | Fully asynchronous, linearly scalable data‑engineering architecture |
| License | Apache license 2.0 |

## 3. Intended Use Cases

- **Fine‑tuning LLMs** for vulnerability triage and severity prediction.
- **Temporal trend analysis** of vulnerability disclosures.
- **Retrieval‑Augmented Generation (RAG)** and autonomous **AI‑Agent** pipelines.
- **Real‑time threat‑intelligence** enrichment services.
- **Automated penetration‑testing** (pentest) orchestration.

> **Benchmark Note**\
> Early experiments with *Llama 3.2* and *Gemma* models achieved **94 % accuracy** on CVE class‑prediction tasks after full fine‑tuning on this dataset.

## 4. Dataset Structure

Each dialogue is stored as a single **JSON Lines (`.jsonl`)** object with **three top‑level keys**:

```json
{
"System": "You are a cybersecurity expert specializing in penetration testing, vulnerability research, and exploit development. Provide comprehensive technical analysis of CVE vulnerabilities with academic rigor and practical exploitation insights.",
"User": "Provide a comprehensive technical analysis of CVE‑2010‑3763, including exploitation vectors, impact assessment, and remediation strategies.",
"Assistant": "## CVE‑2010‑3763 Vulnerability Details

### CVE Metadata
- **CVE ID**: CVE‑2010‑3763
- **State**: PUBLISHED
..."
}
```

### Field Reference

| Key | Type | Description |
|-------------|--------|---------------------------------------------------------------------|
| `System` | string | System prompt that frames the assistant’s role and response style. |
| `User` | string | End‑user request or question. |
| `Assistant` | string | Model answer containing enriched CVE analysis and metadata. |

> **Note**: Multi‑turn conversations are represented as separate JSONL lines that share the same `System` context while `User` and `Assistant` evolve turn by turn.

## 5. Processing Pipeline. Processing Pipeline

1. **Source Aggregation** – CVE XML feeds (4.0) + JSON feeds (5.0+).
2. **Asynchronous Parsing** – Custom Rust & Python pipeline (Tokio + asyncio) for 100 % parsing success.
3. **Enrichment Layer** – CVSS scoring, CWE classification, product‑matrix generation.
4. **Conversation Generation** – Expert prompts injected to produce System / User / Assistant structure.
5. **Validation & QA** – Schema checks, de‑duplication, manual spot‑checks.

## 6. Quick Start

### Load with 🤗 `datasets`

```python
from datasets import load_dataset

cve_chat = load_dataset("<username>/<repo_name>", split="train")
print(cve_chat[0])
```

### Finetune Example (PEFT & QLoRA)

```bash
python train.py \
--model "meta-llama/Meta-Llama-3-8B" \
--dataset "<username>/<repo_name>" \
--peft lora \
--bits 4
```

## 7. Data Splits

| Split | Records | Notes |
| ------------ | ------- | ----- |
| `train` | 240 000 | 80 % |
| `validation` | 30 000 | 10 % |
| `test` | 27 441 | 10 % |

## 8. Contact

Contributions, feedback, and pull requests are warmly welcomed!