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.
Instrucciones de origen · Vista previa de solo lectura
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.
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)
Step 2: Parse PDF Structure with pdf-parser
Examine suspicious objects identified by PDFiD:
# 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:
# 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 extractionimport 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 objectfor 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:
# 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
Step 5: Extract Embedded Files and URLs
Pull out embedded executables and linked resources:
# Extract embedded files from PDFimport 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 headerwithopen("suspect.pdf", "rb") as f:
data = f.read()
# Search for embedded PE files
offset = 0whileTrue:
pos = data.find(b'MZ', offset)
if pos == -1:
break# Verify PE signatureif 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()
withopen(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 inset(urls):
print(f"URL: {url}")
Step 6: Generate Analysis Report
Document all findings from the PDF analysis:
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
Key Concepts
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
Tools & Systems
PDFiD: Didier Stevens' tool for scanning PDF documents for suspicious keywords and structures without parsing the full document
pdf-parser: Companion tool to PDFiD for detailed PDF object extraction, stream decompression, and content analysis
peepdf: Python-based PDF analysis tool providing interactive shell for object inspection and JavaScript extraction
QPDF: PDF transformation tool for linearizing, decrypting, and restructuring PDFs for easier analysis
scdbg: Shellcode analysis tool that emulates x86 shellcode execution and logs API calls
Common Scenarios
Scenario: Triaging a Phishing PDF with Embedded JavaScript
Context: Email gateway flagged a PDF attachment with suspicious JavaScript indicators. The security team needs to determine if it contains an exploit or a social engineering redirect.
Approach:
Run PDFiD to confirm /JS, /JavaScript, and /OpenAction presence and counts
Use pdf-parser to extract the OpenAction object and follow its reference chain
Extract the JavaScript code from the referenced stream object (apply FlateDecode filter)
Deobfuscate the JavaScript (decode hex strings, resolve eval chains)
Determine if the script exploits a PDF reader vulnerability (check for heap spray, ROP chains) or performs a redirect
Extract all URLs, IPs, and embedded files as IOCs
Classify the sample: exploit (specific CVE) or social engineering (redirect/phishing)
Pitfalls:
Opening the PDF in a standard reader instead of analyzing it with command-line tools
Missing JavaScript hidden inside Object Streams (/ObjStm) that PDFiD detects but simple parsers miss
Not decompressing streams before analysis (FlateDecode, ASCIIHexDecode, ASCII85Decode filters)
Assuming the absence of /JS means no JavaScript; code can be embedded in form fields (/AcroForm with /XFA)
