---
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!