Duplicate from AlicanKiraz0/All-CVE-Records-Training-Dataset

Co-authored-by: Alican Kiraz <[email protected]>

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README.md

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+---
+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)
+
+&#x20;
+
+## 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!

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