Individual skill
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
internal/tools/skills/data/malware-analysis/analyzing-malicious-pdf-with-peepdf/SKILL.md
Source commit d42f381ad34843e71a42658e5c57290c713e85af
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- Yes
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- Observed behavior
- Not checked yet
- Last checked
- Nov 7, 2026
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- Repository stars (not skill ratings)
- 1,313
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing Repository topic
- malware-analysis Declared skill tag
- pdf Declared skill tag
- peepdf Declared skill tag
- pdfid Declared skill tag
- pdf-parser Declared skill tag
- static-analysis Declared skill tag
- reverse-engineering Declared skill tag
- dfir Declared skill tag
- engineering Derived category
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SKILL.mdcore · 4,337 bytes
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# Analyzing Malicious PDF with peepdf
## When to Use
- When triaging suspicious PDF attachments from phishing emails
- During malware analysis of PDF-based exploit documents
- When extracting embedded JavaScript, shellcode, or executables from PDFs
- For forensic examination of weaponized document artifacts
- When building detection signatures for PDF-based threats
## Detection Gaps & Validation
- **Hex-escaped names dodge pdfid keyword counts.** `/JavaScript` written as `/J#61vaScript` (and `/OpenAction`, `/Launch` similarly) won't increment pdfid's counters, so pdfid alone undercounts. Normalize names and re-scan with `pdf-parser.py --search` before concluding the PDF is clean.
- **Objects hide inside object streams.** Malicious JS/actions packed in compressed `/ObjStm` are invisible to surface keyword scans; decompress with `pdf-parser.py -O` (or peepdf object listing) to expose them.