Analyzing Malicious Pdf With PeepdfSAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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

| Concept | Description |
|---------|-------------|
| /OpenAction | Automatic action executed when PDF is opened |
| /JavaScript /JS | Embedded JavaScript code in PDF objects |
| /Launch | Action that launches external applications |
| /EmbeddedFile | File embedded within the PDF structure |
| FlateDecode | zlib compression filter used to hide content |
| Object Streams | PDF objects stored in compressed streams |

## Tools & Systems

| Tool | Purpose |
|------|---------|
| peepdf / peepdf-3 | Interactive PDF analysis with JS emulation |
| pdfid.py | Quick triage scanning for suspicious keywords |
| pdf-parser.py | Deep object-level PDF parsing |
| VirusTotal | Hash lookup and AV detection cross-reference |
| CyberChef | Decode and transform extracted payloads |

## 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]
```

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (1)

md5 = hashlib.md5()

Gates applied: no_behavioural_pass.