Malicious Pdf

VERIFIED

πŸ’€ Generate malicious PDF test files for testing phone-home callbacks, SSRF, XSS, NTLM credential theft, and data exfiltration in PDF viewers, converters, and web applications. Can be used with Burp Collaborator or Interact.sh

Downloads 0
Stars 3.7K
Version 1.0.0
Quality 100/100 Β· Excellent

Install with one command

$ npx skills add jonaslejon/malicious-pdf

Decision summary

Production-ready for Document processing

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100
Readiness

Best for

  • Document processing workflows
  • Claude Code teams
  • teams that value GitHub adoption signals

Not ideal for

  • teams that need a vendor-supported SLA
  • high-compliance environments without internal security review

Risk notes

  • No major risk signals from current metadata

Quality profile

Excellent candidate for agent workflows

High-confidence pick with strong adoption and healthy maintenance signals.

100
GitHub stars
3.7K
Freshness
4d ago
Install ready
Yes
License
BSD-2-Clause

Workflow fit

Use this skill in these scenarios

Stack fit

Add it to a complete workflow

Overview

πŸ’€ Generate malicious PDF test files for testing phone-home callbacks, SSRF, XSS, NTLM credential theft, and data exfiltration in PDF viewers, converters, and web applications. Can be used with Burp Collaborator or Interact.sh

Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, RAG, or developer-tool signals. Protocol-server projects are excluded from automated imports.

Platform Compatibility

pythonFULL
pdfFULL

Technical Details

Version
1.0.0
License
BSD-2-Clause
Last Updated
6/6/2026
Published
6/5/2026

Frameworks & Tools

PythonPDF

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Author

J

jonaslejonβœ“

@jonaslejon

Platform Fit

Health Signals

GitHub stars
3.7K
Quality score
67/100
Last GitHub push
Jun 4, 2026
Framework hints
2
OpenAgentSkill views
2
Install copies
0
Outbound clicks
0

Community Signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & Safety

  • β€”Open source (public GitHub repo)
  • β€”AI static analysis passed
  • β€”License: BSD-2-Clause
  • β€”Manually verified by team