Introduction
In this tutorial, we'll explore how to analyze and detect self-spreading malware hidden within Microsoft Word documents that can hijack Microsoft Copilot. This technique demonstrates how malicious code can persist across document usage and automatically propagate. Understanding these attack vectors is crucial for security professionals working with Microsoft Office environments.
Prerequisites
- Basic understanding of Microsoft Word document structure (DOCX format)
- Python programming knowledge
- Access to a Windows system with Python 3.8+ installed
- Microsoft Word (any version with .docx support)
- pip installed for Python package management
Step-by-Step Instructions
Step 1: Set Up the Environment
First, we need to install the required Python libraries to work with DOCX files and analyze their content.
Install Required Libraries
pip install python-docx olefileWhy: The python-docx library allows us to read and manipulate DOCX files, while olefile helps us examine the underlying OLE (Object Linking and Embedding) structures that may contain malicious code.
Step 2: Create a Basic Document Analyzer
We'll create a Python script that can analyze a Word document's internal structure to detect potential malicious elements.
Basic Document Analysis Script
import docx
import olefile
import os
def analyze_docx(file_path):
print(f"Analyzing document: {file_path}")
# Check if file is a valid DOCX
if not file_path.endswith('.docx'):
print("File is not a .docx document")
return
# Try to open the document
try:
doc = docx.Document(file_path)
print(f"Document has {len(doc.paragraphs)} paragraphs")
# Check for embedded objects
print("Checking for embedded objects...")
ole = olefile.OleFileIO(file_path)
if ole:
print("Document contains OLE structures")
# List all streams
for stream in ole.listdir():
print(f"Stream: {stream}")
# Check for potential malicious streams
if any(keyword in stream[0].lower() for keyword in ['macro', 'vba', 'ole']) or \
stream[0].endswith('VBA'):
print(f"\nPotential malicious stream detected: {stream}")
ole.close()
except Exception as e:
print(f"Error analyzing document: {e}")
# Example usage
if __name__ == "__main__":
analyze_docx("test_document.docx")Why: This script establishes a foundation for detecting embedded structures that could contain malicious code, particularly VBA macros or OLE objects that are commonly used in document-based attacks.
Step 3: Detect Macro-Related Threats
Next, we'll enhance our script to specifically look for VBA macros that might be hiding malicious code.
Enhanced Macro Detection
import docx
import olefile
import re
def detect_macros(file_path):
print(f"Checking for macros in: {file_path}")
# Check for VBA project structure
ole = olefile.OleFileIO(file_path)
if not ole:
print("No OLE structure found")
return
# Look for VBA streams
vba_streams = []
for stream in ole.listdir():
if 'vba' in stream[0].lower() or 'macro' in stream[0].lower():
vba_streams.append(stream)
print(f"Found VBA stream: {stream}")
# Try to read the stream content
try:
content = ole.openstream(stream).read()
print(f"Stream size: {len(content)} bytes")
# Look for suspicious patterns
suspicious_patterns = [
r'\bShell\b',
r'\bCreateObject\b',
r'\bExec\b',
r'\bRun\b',
r'\bWScript\.Shell\b',
r'\bHttp\b',
r'\bWinHttp\b'
]
for pattern in suspicious_patterns:
if re.search(pattern, content.decode('utf-8', errors='ignore'), re.IGNORECASE):
print(f"\nSuspicious pattern detected: {pattern}")
except Exception as e:
print(f"Could not read stream: {e}")
ole.close()
return vba_streamsWhy: This step specifically targets VBA macro code, which is a common vector for document-based malware. The script searches for suspicious function calls that might indicate malicious intent.
Step 4: Create a Worm Detection System
Now we'll build a system that can detect the specific propagation behavior mentioned in the security report.
Worm Propagation Detection
import docx
import re
# Define patterns that indicate self-propagation behavior
PROPAGATION_PATTERNS = [
r'\bActiveDocument\.SaveAs\b',
r'\bDocument\.SaveAs\b',
r'\bApplication\.Run\b',
r'\bThisDocument\.Open\b',
r'\bDocument\.Open\b',
r'\b\$\(\s*\w+\s*\)\s*\w+\s*\(.*?\)\b', # Pattern matching JavaScript-like calls
r'\bMicrosoft\.Office\.Word\.Application\b',
r'\bWord\.Application\b'
]
# Pattern for Copilot-related injection
COPILOT_PATTERNS = [
r'\bCopilot\b',
r'\bMicrosoft\s+Copilot\b',
r'\bAI\s+Assistant\b',
r'\bChatGPT\b',
r'\bGPT\b'
]
def detect_worm_behavior(file_path):
print(f"Detecting worm behavior in: {file_path}")
# First, check for macro content
ole = olefile.OleFileIO(file_path)
if not ole:
print("No OLE structure found")
return
# Check all VBA streams
for stream in ole.listdir():
if 'vba' in stream[0].lower():
try:
content = ole.openstream(stream).read()
content_str = content.decode('utf-8', errors='ignore')
# Check for propagation patterns
print(f"\nChecking stream: {stream}")
for pattern in PROPAGATION_PATTERNS:
matches = re.findall(pattern, content_str, re.IGNORECASE)
if matches:
print(f"\nPropagation pattern found: {pattern}")
print(f"Matches: {matches}")
# Check for Copilot-related injection
for pattern in COPILOT_PATTERNS:
matches = re.findall(pattern, content_str, re.IGNORECASE)
if matches:
print(f"\nCopilot-related pattern found: {pattern}")
print(f"Matches: {matches}")
except Exception as e:
print(f"Could not analyze stream {stream}: {e}")
ole.close()
return TrueWhy: This system specifically targets the propagation mechanisms described in the article - how malware spreads through document reuse and potentially hijacks Copilot functionality by embedding code that executes automatically.
Step 5: Test the Detection System
Let's create a simple test to see if our detection system works properly.
Testing Script
def create_test_document():
# Create a simple test document
doc = docx.Document()
doc.add_paragraph("This is a test document")
doc.add_paragraph("It contains some harmless content")
# Save the document
doc.save("test_document.docx")
print("Created test document")
# Run our analysis
analyze_docx("test_document.docx")
detect_macros("test_document.docx")
detect_worm_behavior("test_document.docx")
if __name__ == "__main__":
create_test_document()Why: This final step demonstrates how to use our detection system in practice, allowing us to verify that it correctly identifies document structures and potential threats.
Summary
In this tutorial, we've built a comprehensive document analysis system that can detect malicious patterns within Microsoft Word documents, including those that might propagate through document reuse and potentially hijack Copilot functionality. We've covered:
- Setting up the environment with necessary Python libraries
- Basic document structure analysis
- Macro detection capabilities
- Worm propagation behavior identification
- Testing our system with sample documents
This system provides security professionals with tools to identify document-based threats that exploit Microsoft Office environments, helping to protect against the specific attack patterns described in the security report.
