Nemesispro/Exploit_Database_Dataset

---
license: mit
language:
- en
tags:
- cybersecurity
pretty_name: sunny thakur
size_categories:
- 1K<n<10K
---
# Exploit Database Dataset
# Overview
```sql
This dataset contains 1400 curated entries of cybersecurity vulnerabilities, designed for training a Red Team GPT model.
It includes detailed records of vulnerabilities from 2021-2025, sourced from Exploit-DB, CVE details (nvd.nist.gov), and recent web sources (e.g., CISA KEV catalog, The Hacker News).
The dataset is structured to support AI-driven penetration testing, vulnerability research, and cybersecurity analysis.
```
# Dataset Structure
Each entry follows a consistent JSON format with the following fields:
```sql
id: Unique CVE identifier (e.g., CVE-2024-30157).
title: Brief description of the vulnerability and affected system (e.g., "Windows Task Scheduler - RCE").
date: Publication date of the vulnerability (YYYY-MM-DD).
type: Type of vulnerability (e.g., Remote Code Execution, Stored XSS, Denial of Service).
platform: Affected platform (e.g., Web, Software, Network, Mobile, IoT).
poc: Proof-of-Concept exploit code or command, formatted for use in tools like Burp Suite, Metasploit, or CLI.
description: Concise summary of the vulnerability and its impact.
```
Example Entry
```javascript
{
"id": "CVE-2024-30157",
"title": "Windows Task Scheduler - RCE",
"date": "2024-06-11",
"type": "Remote Code Execution",
"platform": "Software",
"poc": "taskscheduler_exploit --cmd whoami",
"description": "Task Scheduler flaw in Windows allows unauthenticated RCE."
}
```
Dataset Details
```sql
Total Entries: 230 (split across three JSON files: 1171-1250, 1251-1330, 1331-1400).
Vulnerability Types: Includes Remote Code Execution (RCE), Stored Cross-Site Scripting (XSS), Denial of Service (DoS), and others.
Platforms: Covers Web, Software, Network, Mobile, and IoT systems.
Sources:
Exploit-DB (exploit-db.com)
CVE Details (nvd.nist.gov)
Web sources (e.g., CISA Known Exploited Vulnerabilities, The Hacker News)

PoC Realism: PoCs are crafted based on known exploit patterns where direct exploits are unavailable, ensuring practical applicability for pentesting.
Time Range: Vulnerabilities from 2021 to 2025, focusing on recent and high-impact issues.
```
# Usage
This dataset is ideal for:
```
AI Model Training: Fine-tuning Red Team GPT models for vulnerability detection and exploit generation.
Penetration Testing: Providing realistic PoCs for testing systems in controlled environments.
Security Research: Analyzing trends in vulnerabilities across platforms and types.
Educational Purposes: Training cybersecurity professionals on exploit patterns and mitigation strategies.
```
# Prerequisites
```
JSON parsing tools (e.g., Python with json library, jq).
Penetration testing tools (e.g., Burp Suite, Metasploit) for executing PoCs.
Familiarity with cybersecurity concepts and ethical hacking practices.
```
Example Usage
```java
import json

with open('exploit_dataset.json', 'r') as file:
data = json.load(file)

for entry in data:
print(f"CVE: {entry['id']}, Type: {entry['type']}, PoC: {entry['poc']}")
```
# Ethical Considerations
```
Responsible Use: This dataset is for educational and authorized testing purposes only.
Unauthorized use of PoCs against systems without permission is illegal and unethical.
Controlled Environments: Test PoCs in isolated, controlled environments to avoid unintended harm.
Compliance: Ensure compliance with local laws and organizational policies when using this dataset.
```
# Contributing

To contribute additional vulnerabilities, submit a pull request with JSON entries following the specified format. Ensure entries are sourced from reputable databases (e.g., Exploit-DB, NVD) and include realistic PoCs.

# License

This dataset is provided under the MIT License. See LICENSE file for details.
# Contact
For questions or feedback, contact the dataset maintainer at [e-mail]