license: mit
task_categories:
- text-classification
- question-answering
language: - en
tags: - security
- codes
pretty_name: sunny thakur
size_categories: - n<1K
Vulnerable Programming Dataset
Overview
The Vulnerable Programming Dataset is a comprehensive collection of 550 unique code vulnerabilities across 10 programming languages: Python, JavaScript, PHP, Java, Ruby, Go, TypeScript, C++, SQL, and C. Designed for cybersecurity professionals, red teamers, pentesters, and developers, this dataset highlights unconventional vulnerabilities such as insecure interprocess communication, misconfigured rate limiting, insecure dependency pinning, and logic flaws. Each entry provides a code snippet, description, and references to OWASP Top 10 or CWE for further study.
This dataset serves as an educational and testing resource to understand, identify, and mitigate security flaws in software development and penetration testing scenarios.
Dataset Structure
The dataset is provided in JSON format (vulnerabilities.json) with the following schema for each entry:
id: Unique identifier for the vulnerability (1–550).
language: Programming language (e.g., Python, JavaScript, PHP).
vulnerability: Name of the vulnerability (e.g., "Insecure Dynamic Signal Registration").
description: Brief explanation of the vulnerability and its risks.
code: Code snippet demonstrating the vulnerability.
references: Links to OWASP Top 10, CWE, or language-specific security guidelines.
Example entry:
{
"id": 1,
"language": "Python",
"vulnerability": "Insecure Dynamic Signal Registration",
"description": "Registering signal handlers with unvalidated callbacks.",
"code": "import signal\ndef register_signal(sig, callback):\n signal.signal(sig, callback)",
"references": [
"CWE-479: https://cwe.mitre.org/data/definitions/479.html"
]
}
Usage
This dataset can be used for:
Education: Learn about common and unconventional vulnerabilities in various programming languages.
Penetration Testing: Simulate and test vulnerable code in controlled environments.
Secure Coding Training: Understand anti-patterns to avoid in software development.
Tool Development: Build or enhance static analysis tools, linters, or security scanners.
Explore the Dataset:Open vulnerabilities.json to review the entries or use a JSON parser to filter by language or vulnerability type.
Analyze Vulnerabilities:Use the provided code snippets in a sandboxed environment to study their behavior and test mitigation strategies.
Integrate with Tools:Import the JSON data into your security tools for automated analysis or training purposes.
Note: Always test code in a secure, isolated environment to prevent unintended consequences.
Contribution Guidelines
We welcome contributions to expand or improve the dataset. To contribute:
Fork the repository.
Add new vulnerabilities or enhance existing entries, ensuring no overlap with current entries (1–550).
Follow the existing JSON schema and include references to OWASP, CWE, or reputable sources.
Submit a pull request with a clear description of your changes.
Please adhere to the following:
Ensure code snippets are minimal and reproducible.
Provide accurate and concise descriptions.
Cite authoritative references for each vulnerability.
License
This dataset is licensed under the MIT License. You are free to use, modify, and distribute the dataset, provided you include the original license and attribution.
Contact
For questions, suggestions, or issues, please open an issue on the GitHub repository or contact the maintainers at [email protected].
Acknowledgments
This dataset was created to advance cybersecurity education and secure coding practices. Special thanks to the open-source community, OWASP, and CWE for their invaluable resources.
Disclaimer: This dataset is for educational and testing purposes only. Do not use the provided code in production environments, as it contains intentional vulnerabilities that could compromise system security.