What it does. Perform static analysis of malicious PDF documents using peepdf, pdfid, Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malicious-pdf-with-peepdf, or copy the skill folder into ~/.claude/skills/analyzing-malicious-pdf-with-peepdf/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/analyzing-malicious-pdf-with-peepdf/SKILL.md

SKILL.md (verbatim)

name: analyzing-malicious-pdf-with-peepdf
description: Perform static analysis of malicious PDF documents using peepdf, pdfid,
  and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
  Use when triaging a suspicious PDF attachment from a phishing email, analyzing a
  PDF-based exploit document, or building detection signatures for weaponized PDF
  threats.
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- pdf
- peepdf
- pdfid
- pdf-parser
- static-analysis
- reverse-engineering
- dfir
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1204.002
- T1059.007
- T1027
- T1106

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

Prerequisites

• Python 3.8+ with peepdf-3 installed (pip install peepdf-3)
• pdfid.py and pdf-parser.py from Didier Stevens suite
• Isolated analysis environment (VM or sandbox)
• Optional: PyV8 for JavaScript emulation within peepdf
• Optional: Pylibemu for shellcode analysis

Workflow

1. Triage with pdfid: Scan PDF for suspicious keywords (/JS, /JavaScript, /OpenAction, /Launch, /EmbeddedFile).
2. Interactive Analysis: Open PDF in peepdf interactive mode to explore object structure.
3. Identify Suspicious Objects: Locate objects containing JavaScript, streams, or encoded data.
4. Extract Content: Dump suspicious streams and decode filters (FlateDecode, ASCIIHexDecode).
5. Deobfuscate JavaScript: Analyze extracted JS for shellcode, heap sprays, or exploit code.
6. Check VirusTotal: Use peepdf vtcheck to cross-reference file hash with AV detections.
7. Generate IOCs: Extract URLs, domains, hashes, and shellcode signatures.

Key Concepts

Tools & Systems

Output Format

Analysis Report: PDF-MAL-[DATE]-[SEQ]
File: [filename.pdf]
SHA-256: [hash]
Suspicious Keywords: [/JS, /OpenAction, etc.]
Objects with JavaScript: [Object IDs]
Extracted URLs: [List]
Shellcode Detected: [Yes/No]
Embedded Files: [Count and types]
VirusTotal Detections: [X/Y engines]
Risk Level: [Critical/High/Medium/Low]

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

Malicious PDF Analysis Reference

peepdf Installation

# Python 3 version
pip install peepdf-3

# From source
git clone https://github.com/jesparza/peepdf.git
cd peepdf && pip install -r requirements.txt

peepdf CLI Usage

# Basic analysis (loose mode, force parsing)
peepdf -f -l malicious.pdf

# Interactive mode
peepdf -i malicious.pdf

# Batch script execution
peepdf -s commands.txt malicious.pdf

# JSON output
peepdf -j malicious.pdf

peepdf Interactive Commands

pdfid.py Usage

# Basic scan
pdfid.py malicious.pdf

# Additional disarm indicators
pdfid.py -e malicious.pdf

# Scan directory
pdfid.py -r /samples/

pdfid Suspicious Keywords

pdf-parser.py Usage

# Document statistics
pdf-parser.py --stats malicious.pdf

# Extract specific object
pdf-parser.py -o 10 malicious.pdf

# Extract and decode filters
pdf-parser.py -o 10 -f malicious.pdf

# Dump decoded stream to file
pdf-parser.py -o 10 -f -d extracted.bin malicious.pdf

# Search for keyword
pdf-parser.py --search "/JavaScript" malicious.pdf

# Search by type
pdf-parser.py --type "/Action" malicious.pdf

Common CVEs in PDF Exploits

Shellcode Detection Patterns

VirusTotal Check via peepdf

PPDF> vtcheck
MD5: abc123...
Detections: 45/72

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