license: mit
language:
- en
tags: - cybersecurity's
- web
- security
pretty_name: sunnythakur
size_categories: - n<1K
Polyglot Payloads Dataset for Cybersecurity Training
Overview
This dataset, polyglot_payloads.jsonl, is a curated collection of 500 polyglot payloads designed for training AI models in cybersecurity, specifically for red team operations and vulnerability detection. The dataset includes payloads targeting common web vulnerabilities such as Cross-Site Scripting (XSS), SQL Injection (SQLi), Local File Inclusion (LFI), Remote Code Execution (RCE), and Server-Side Template Injection (SSTI). Each entry is formatted in JSONL (JSON Lines) to facilitate processing and integration into machine learning pipelines.
Dataset Structure
The dataset is structured in JSONL format, where each line represents a single payload entry with the following fields:
payload: The malicious or test input string designed to exploit a specific vulnerability.
type: The category of the vulnerability the payload targets (e.g., XSS, SQLi, LFI, RCE, SSTI).
description: A brief explanation of the payload's purpose and potential impact.
Example Entry
{"payload":"<script>alert(1)</script>","type":"XSS","description":"Basic JavaScript alert payload for cross-site scripting."}
Dataset Details
Total Entries: 500
Vulnerability Types:
XSS: Payloads that inject malicious scripts into web pages to execute in a user's browser.
SQLi: Payloads designed to manipulate SQL queries, often to bypass authentication or extract data.
LFI: Payloads targeting local file inclusion vulnerabilities to access sensitive system files.
RCE: Payloads that enable arbitrary code execution on the target server.
SSTI: Payloads exploiting server-side template engines to execute arbitrary code or access sensitive data.
File Format: JSONL (one JSON object per line)
File Name: polygot_payloads.jsonl
Usage
This dataset is intended for training AI models to detect and classify malicious inputs in cybersecurity applications. It can be used for:
Machine Learning: Train models to identify and categorize web vulnerabilities based on input patterns.
Red Team Exercises: Simulate attacks to test the resilience of web applications and validate detection mechanisms.
Security Research: Analyze payload patterns to develop defensive strategies or improve vulnerability scanners.
Bug Hunting: Assist in creating proof-of-concept (PoC) exploits to demonstrate vulnerabilities to vendors in a clear, real-world context.
Loading the Dataset
To load the dataset in Python, you can use the following example:
import json
payloads = []
with open('polygot_payloads.jsonl', 'r') as file:
for line in file:
payloads.append(json.loads(line.strip()))
# Example: Print first payload
print(payloads[0])
Example Use Case
Train a natural language processing (NLP) model to classify payloads by their vulnerability type. The payload field can be used as input, with type as the target label and description for contextual understanding during analysis or reporting.
Security Considerations
Ethical Use: This dataset is intended for educational and defensive purposes only. Do not use these payloads to harm systems or networks without explicit authorization.
Controlled Environment: Test payloads in a sandboxed or controlled environment to avoid unintended consequences.
Responsible Disclosure: When using payloads for bug hunting, follow responsible disclosure practices to report vulnerabilities to vendors securely.
Contribution
Contributions to expand or refine this dataset are welcome. Please submit additional payloads or improvements via pull requests, ensuring they adhere to the JSONL format and include clear descriptions. Focus on novel payloads that target emerging vulnerabilities or enhance coverage of existing ones.
License
This dataset is provided under the MIT License. You are free to use, modify, and distribute it, provided you include the original license and attribution to the authors.
Contact
For questions or feedback, contact the dataset maintainers via the repository's issue tracker or email [email protected]