jonaslejon/malicious-pdf

💀 Generate malicious PDF test files for testing phone-home callbacks, SSRF, XSS, NTLM credential theft, and data exfiltration in PDF viewers, converters,...

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Summary Information

Updated 38 minutes ago
Added to GitGenius on September 16th, 2026
Created on August 17th, 2021
Open Issues & Pull Requests: 4 (+0)
GitHub issues: Enabled
Number of forks: 573
Total Stargazers: 4,362 (+0)
Total Subscribers: 64 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 179.5 days
Mean response time: 149.9 days
90th percentile: 210.7 days
Tracked items: 4

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Open issues: 2
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 349 days
Stale 30+ days: 2
Stale 90+ days: 2

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Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Malicious-pdf is a Python tool for generating malicious PDF test files used in security testing and penetration testing.

The tool addresses the need to test how PDF viewers, converters, and web applications handle malicious PDF payloads. It generates PDF files crafted to trigger specific security vulnerabilities including phone-home callbacks, server-side request forgery (SSRF), cross-site scripting (XSS), NTLM credential theft, and data exfiltration. The generated files can be used with external callback services like Burp Collaborator or Interact.sh to detect whether applications process the malicious content and leak sensitive information.

This tool is suited for security researchers, penetration testers, and bug bounty hunters who need to test the security posture of applications that accept PDF uploads or process PDF files. It is particularly valuable when assessing web applications, document converters, and PDF viewers for their handling of embedded malicious content. Organizations conducting internal security assessments can use it to validate that their systems properly sanitize or reject dangerous PDF payloads.

The project shows active maintenance with regular updates addressing new attack vectors and improving payload generation capabilities. Development includes responsiveness to user-reported issues and feature requests for additional payload types. The codebase demonstrates ongoing refinement of the PDF generation logic to ensure payloads remain effective across different PDF processing environments.