Analyzing Malicious Pdf With Peepdf

---
name: analyzing-malicious-pdf-with-peepdf
description: Use when perform static analysis of malicious PDF documents using peepdf,
  pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious
  objects. Use when performing static analysis of malicious pdf documents using peepdf,
  pdfid,.
domain: cybersecurity
tags:
- malware-analysis
- pdf
- peepdf
- pdfid
- pdf-parser
- static-analysis
- reverse-engineering
- dfir
subdomain: malware-analysis
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
category: cybersecurity
---

# Analyzing Malicious Pdf With Peepdf

## Overview

Cybersecurity skill for analyzing malicious pdf with peepdf. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "analyzing malicious pdf with peepdf"
- "Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-"


- 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


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## 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

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Scope the Analysis** — Define what malicious pdf artifacts or data sources to examine and the investigation timeline.
2. **Preserve Evidence** — Create forensic copies of relevant data. Maintain chain of custody documentation.
3. **Extract Key Indicators** — Use peepdf to parse and extract relevant malicious pdf data points from collected artifacts.
4. **Correlate Findings** — Cross-reference extracted data with other sources (threat intel, logs, timelines).
5. **Build Timeline** — Construct a chronological sequence of events related to malicious pdf.
6. **Document Analysis** — Write findings report with evidence, conclusions, and recommendations.

## Tools

- **peepdf** — Primary tool for this skill
- **Forensic Toolkit** — Evidence collection and analysis
- **Timeline Tools** — Chronological event reconstruction
- **Log Analysis Platform** — Centralized log parsing and search


## Process

1. **Scope** — Define research questions, identify data sources, set time boundaries
1. **Gather** — Collect data from primary sources, APIs, and public records
1. **Synthesize** — Analyze findings, identify patterns, produce actionable report

## Verification

- [ ] All malicious pdf procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |