Analyzing Pdf Malware With PdfidSAFE

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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-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.

  '
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- PDF-analysis
- document-malware
- PDFiD
- static-analysis
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1204.002
- T1566.001
- T1059.007
- T1027
---

# 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
```

```python
# Python script for comprehensive PDF JavaScript extraction
import subprocess
import re

# Extract all streams and search for JavaScript
result = subprocess.run(
    ["pdf-parser", "--stats", "suspect.pdf"],
    capture_output=True, text=True
)

# Find object IDs containing JavaScript references
js_objects = []
for line in result.stdout.split('\n'):
    if '/JavaScript' in line or '/JS' in line:
        obj_id = re.search(r'obj (\d+)', line)
        if obj_id:
            js_objects.append(obj_id.group(1))

# Extract each JavaScript-containing object
for obj_id in js_objects:
    result = subprocess.run(
        ["pdf-parser", "--object", obj_id, "--filter", "--raw", "suspect.pdf"],
        capture_output=True, text=True
    )
    print(f"\n=== Object {obj_id} ===")
    print(result.stdout[:2000])
```

### Step 4: Analyze Embedded Shellcode

Extract and examine shellcode from PDF exploits:

```bash
# Extract raw stream data for shellcode analysis
pdf-parser --object 7 --filter --raw --dump shellcode.bin suspect.pdf

# Analyze shellcode wi

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

declared (1 observation(s))

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (2)

"/SubmitForm": "Form submission (data exfiltration)",

decoded = bytes.fromhex(match.group(1)).decode("utf-8", errors="replace")

Gates applied: no_behavioural_pass.