piyjsd3/Cybersecurity-LLM-CVE

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language:
- en
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**2025.06.07 Updated data code** :https://github.com/Bouquets-ai/Data-Processing/blob/main/CVE-Data.py

Change 121 lines of code (keyword="CVE-2025") to obtain the required CVE time

93 lines of code (json record=) to change the required format

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Cybersecurity-LLM-CVE Dataset Introduction 🚀

Overview 🛡️

An open-source cybersecurity vulnerability dataset designed for training/evaluating Large Language Models (LLMs) in security domains. Covers all public CVE IDs from January 1, 2021 to April 9, 2025, with structured data including vulnerability details (affected products) and disclosure dates.

Key Highlights ✨

Comprehensive 🌍: 5-year global vulnerability records (software/hardware/protocols)

Authoritative Integration 🔍: Data sourced from NVD, CVE lists, and community contributions, double-verified

Usage Declaration 💡

Typical Use Cases 🛠️

🧠 LLM-powered vulnerability patch generation

🤖 Automated threat intelligence analysis

🔧 Penetration testing toolkit enhancement

Final Notes 🌟

💬 Feel free to leave suggestions in the community forum! I'll keep optimizing based on your feedback~ 🥰

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language:
- 英语
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**2025年6月7日更新的数据代码**：https://github.com/Bouquets-ai/Data-Processing/blob/main/CVE-Data.py

修改121行代码（关键词为"CVE-2025"）以获取所需的CVE时间信息；修改93行代码（对应json记录字段）以调整至目标格式

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**网络安全-大语言模型（Large Language Model，LLM）-通用漏洞披露（Common Vulnerabilities and Exposures，CVE）数据集简介 🚀**

### 概述 🛡️
本数据集为开源网络安全漏洞数据集，旨在用于训练与评估安全领域的大语言模型。数据集覆盖2021年1月1日至2025年4月9日期间的全部公开CVE编号，包含结构化数据，涵盖漏洞详情（受影响产品）与披露日期。

### 核心亮点 ✨
**全面性 🌍**：收录5年全球范围内的软件、硬件与协议类漏洞记录
**权威整合 🔍**：数据源自国家漏洞数据库（National Vulnerability Database，NVD）、CVE列表及社区贡献，并经过双重校验

### 使用声明 💡

### 典型应用场景 🛠️
🧠 基于大语言模型的漏洞补丁生成
🤖 自动化威胁情报分析
🔧 渗透测试工具集增强

### 最后说明 🌟
💬 欢迎在社区论坛提出宝贵建议！我们将根据您的反馈持续优化数据集~ 🥰