Detect embedded threats and indicators of compromise in PDF files.
npx skills add https://github.com/chenchunrun/onyx-soc --skill pdf-analysis
Please help me install this Agent Skill. Skill: pdf-analysis Source: https://github.com/chenchunrun/onyx-soc/tree/main/skills/pdf-analysis Command: npx skills add https://github.com/chenchunrun/onyx-soc --skill pdf-analysis
PDF files often contain malicious scripts, embedded payloads, or unsafe features that are hard to identify manually, leading to security risks.
Analyze a sample PDF to identify threat indicators and generate a concise security report.
High-intent search queries and answers about installing and using this skill.
You can detect malicious JavaScript in PDFs by applying robust parsing to extract document metadata, identify embedded content, and report indicators of compromise. This process analyzes JavaScript, embedded files, URLs, and CVE features to uncover hidden threats.
The best way to extract metadata and URLs for threat intelligence is to parse the PDF structure for creator information, embedded links, and CVE indicators. This generates structured output suitable for SIEM workflows and automated scanning pipelines.
Yes, you can analyze PDF security threats in automated scanning pipelines using PyMuPDF, Pillow, and pyzbar. This Skill implements robust parsing and metadata extraction to generate structured output for SIEM integration and incident response.
PDF threat detection works with SIEM workflows by generating structured output from parsed metadata, JavaScript, and embedded file analysis. This provides incident responders and threat analysts with concise security reports for rapid triage.
Identifying CVE indicators and unsafe features in PDF documents requires robust parsing to detect JavaScript, embedded payloads, and form data. This Skill reports these threat indicators to support incident response and automated scanning workflows.
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