analyzing-pdf-malware-with-pdfid
Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening…
Skill by mukul975
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: documents
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/analyzing-pdf-malware-with-pdfid
What's inside
- Skill name
analyzing-pdf-malware-with-pdfid- Description
- Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.
- License
- Apache-2.0
- Instructions
- 327 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (4)
- LICENSE
- SKILL.md
- api-reference.md · references/
- agent.py · scripts/
SKILL.md (first lines)
# Analyzing PDF Malware with PDFiD ## When to Use - A suspicious PDF attachment has been flagged by email security or reported by a user - You need to determine if a PDF contains embedded JavaScript, shellcode, or exploit code - Triaging PDF documents before opening them in a sandbox or analysis environment - Extracting embedded executables, scripts, or URLs from malicious PDF objects - Analyzing PDF exploit kits targeting Adobe Reader or other PDF viewer vulnerabilities **Do not use** for analyzing the rendered visual content of a PDF; this is for structural analysis of the PDF file format for malicious objects. ## Prerequisites - Python 3.8+ with Didier Stevens' PDF tools installed (`pip install pdfid pdf-parser`) - peepdf installed for interactive PDF analysis (`pip install peepdf`) - pdftotext from poppler-utils for extracting text content safely - YARA with PDF-specific rules for malware family identification - Isolated analysis VM without a PDF reader installed (prevent accidental opening) - CyberChef for decoding embedded Base64, hex, or deflate streams ## Workflow ### Step 1: Initial Triage with PDFiD Scan the PDF for suspicious keywords and structures: ```bash # Run PDFiD to identify suspicious elements pdfid suspect.pdf # Expected output analysis: # /JS - JavaScript (HIGH risk) # /JavaScript - JavaScript object (HIGH risk) # /AA - Auto-Action triggered on open (HIGH risk) # /OpenAction - Action on document open (HIGH risk) # /Launch - Launch external application (HIGH risk) # /EmbeddedFile - Embedded file (MEDIUM risk) # /RichMedia - Flash content (MEDIUM risk) # /ObjStm - Object stream (used for obfuscation) # /URI - URL reference (contextual risk) # /AcroForm - Interactive form (MEDIUM risk) # Run with extra detail pdfid -e suspect.pdf # Run with disarming (rename suspicious keywords) pdfid -d suspect.pdf ``` ``` PDFiD Risk Assessment: ━━━━━━━━━━━━━━━━━━━━━ HIGH RISK indicators (any count > 0): /JS, /JavaScript -> Embedded JavaScript code /AA -> Automatic Action (triggers without user interaction) /OpenAction -> Code runs when document is opened /Launch -> Can launch external executables /JBIG2Decode -> Associated with CVE-2009-0658 exploit MEDIUM RISK indicators: /EmbeddedFile -> Contains embedded files (could be EXE/DLL) /RichMedia -> Flash/multimedia (Flash exploits) /AcroForm -> Form with possible submit action /XFA -> XML Forms Architecture (complex attack surface) LOW RISK indicators: /ObjStm -> Object streams (obfuscation technique) /URI -> External URL references /Page -> Number of pages (context only) ``` ### Step 2: Parse PDF Structure with pdf-parser Examine suspicious objects identified by PDFiD: ```bash # List all objects referencing JavaScript pdf-parser --search "/JavaScript" suspect.pdf pdf-parser --search "/JS" suspect.pdf # List all objects with OpenAction pdf-parser --search "/OpenAction" suspect.pdf # Extract a specific object by ID (example: object 5) pdf-parser --object 5 suspect.pdf # Extract and decompress stream content pdf-parser --object 5 --filter --raw suspect.pdf # Search for embedded files pdf-parser --search "/EmbeddedFile" suspect.pdf # List all objects with their types pdf-parser --stats suspect.pdf ``` ### Step 3: Extract and Analyze Embedded JavaScript Pull out JavaScript code from PDF objects: ```bash # Extract JavaScript using pdf-parser pdf-parser --search "/JS" --raw --filter suspect.pdf > extracted_js.txt # Alternative: Use peepdf for interactive JavaScript extraction peepdf -f -i suspect.pdf << 'EOF' js_analyse EOF # peepdf interactive commands for JS analysis: # js_analyse - Extract and show all JavaScript code # js_beautify - Format extracted JavaScript # js_eval <object> - Evaluate JavaScript in sandboxed environment # object <id> - Display object content # rawobject <id> - Display raw object bytes # stream <id> - Display decompressed stream # offsets - Show object offsets in file ```
From mukul975/Anthropic-Cybersecurity-Skills, Apache-2.0.
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Signals
- Stars
- 34,941
- Forks
- 4,256
- Open issues
- 24
- Last commit (this folder)
- Jun 1, 2026 (5 months ago)
- Last release
- Jun 22, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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analyzing-pdf-malware-with-pdfid skill. Agent Mart. https://agentmart.dev/skills/mukul975-analyzing-pdf-malware-with-pdfid (last updated 2026-10-24)