Agent skill
analyzing-outlook-pst-for-email-forensics
Analyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments, deleted items, and metadata using libpff, pst-utils, and forensic email analysis tools for legal investigations and incident response.
Install this agent skill to your Project
npx add-skill https://github.com/autohandai/community-skills/tree/main/analyzing-outlook-pst-for-email-forensics
SKILL.md
Analyzing Outlook PST for Email Forensics
Overview
Microsoft Outlook PST (Personal Storage Table) and OST (Offline Storage Table) files are critical evidence sources in digital forensics investigations. PST files store email messages, calendar events, contacts, tasks, and notes in a proprietary binary format based on the MAPI (Messaging Application Programming Interface) property system. Forensic analysis of these files enables recovery of deleted emails (from the Recoverable Items folder), extraction of email headers for tracing message routes, analysis of attachments for malware or exfiltrated data, and reconstruction of communication patterns. Modern PST files use Unicode format with 4KB pages and can grow up to 50GB, while legacy ANSI format is limited to 2GB.
Prerequisites
- libpff/pffexport (open-source PST parser)
- Python 3.8+ with pypff or libratom libraries
- MailXaminer, Forensic Email Collector, or SysTools PST Forensics (commercial)
- Microsoft Outlook (optional, for native PST access)
- Sufficient disk space for extracted content
PST File Locations
| Source | Path |
|---|---|
| Outlook 2016+ Default | %USERPROFILE%\Documents\Outlook Files*.pst |
| Outlook Legacy | %LOCALAPPDATA%\Microsoft\Outlook*.pst |
| OST Cache | %LOCALAPPDATA%\Microsoft\Outlook*.ost |
| Archive | %USERPROFILE%\Documents\Outlook Files\archive.pst |
Analysis with Open-Source Tools
libpff / pffexport
# Export all items from PST file
pffexport -m all evidence.pst -t exported_pst
# Export only email messages
pffexport -m items evidence.pst -t exported_emails
# Export recovered/deleted items
pffexport -m recovered evidence.pst -t recovered_items
# Get PST file information
pffinfo evidence.pst
Python PST Analysis
import pypff
import os
import json
import hashlib
import email
import sys
from datetime import datetime
from collections import defaultdict
class PSTForensicAnalyzer:
"""Forensic analysis of Outlook PST/OST files."""
def __init__(self, pst_path: str, output_dir: str):
self.pst_path = pst_path
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
self.pst = pypff.file()
self.pst.open(pst_path)
self.messages = []
self.attachments = []
self.stats = defaultdict(int)
def process_folder(self, folder, folder_path: str = ""):
"""Recursively process PST folders and extract messages."""
folder_name = folder.name or "Root"
current_path = f"{folder_path}/{folder_name}" if folder_path else folder_name
for i in range(folder.number_of_sub_messages):
try:
message = folder.get_sub_message(i)
msg_data = self.extract_message(message, current_path)
if msg_data:
self.messages.append(msg_data)
self.stats["total_messages"] += 1
except Exception as e:
self.stats["parse_errors"] += 1
for i in range(folder.number_of_sub_folders):
try:
subfolder = folder.get_sub_folder(i)
self.process_folder(subfolder, current_path)
except Exception:
continue
def extract_message(self, message, folder_path: str) -> dict:
"""Extract forensic metadata from a single email message."""
msg_data = {
"folder": folder_path,
"subject": message.subject or "",
"sender": message.sender_name or "",
"sender_email": "",
"creation_time": str(message.creation_time) if message.creation_time else None,
"delivery_time": str(message.delivery_time) if message.delivery_time else None,
"modification_time": str(message.modification_time) if message.modification_time else None,
"has_attachments": message.number_of_attachments > 0,
"attachment_count": message.number_of_attachments,
"body_size": len(message.plain_text_body or b""),
"html_size": len(message.html_body or b""),
}
# Extract transport headers for routing analysis
headers = message.transport_headers
if headers:
msg_data["headers_present"] = True
msg_data["headers_size"] = len(headers)
# Parse key headers
parsed = email.message_from_string(headers)
msg_data["from_header"] = parsed.get("From", "")
msg_data["to_header"] = parsed.get("To", "")
msg_data["date_header"] = parsed.get("Date", "")
msg_data["message_id"] = parsed.get("Message-ID", "")
msg_data["x_originating_ip"] = parsed.get("X-Originating-IP", "")
msg_data["received_headers"] = parsed.get_all("Received", [])
# Process attachments
for j in range(message.number_of_attachments):
try:
attachment = message.get_attachment(j)
att_data = {
"message_subject": msg_data["subject"],
"name": attachment.name or f"attachment_{j}",
"size": attachment.size,
"content_type": "",
}
self.attachments.append(att_data)
self.stats["total_attachments"] += 1
except Exception:
continue
return msg_data
def save_attachments(self, max_size_mb: int = 100):
"""Export attachments to disk for analysis."""
att_dir = os.path.join(self.output_dir, "attachments")
os.makedirs(att_dir, exist_ok=True)
root = self.pst.get_root_folder()
self._save_attachments_recursive(root, att_dir, max_size_mb)
def _save_attachments_recursive(self, folder, att_dir, max_size_mb):
for i in range(folder.number_of_sub_messages):
try:
message = folder.get_sub_message(i)
for j in range(message.number_of_attachments):
att = message.get_attachment(j)
if att.size and att.size < max_size_mb * 1024 * 1024:
name = att.name or f"unknown_{i}_{j}"
safe_name = "".join(c if c.isalnum() or c in ".-_" else "_" for c in name)
path = os.path.join(att_dir, safe_name)
try:
data = att.read_buffer(att.size)
with open(path, "wb") as f:
f.write(data)
except Exception:
continue
except Exception:
continue
for i in range(folder.number_of_sub_folders):
try:
self._save_attachments_recursive(folder.get_sub_folder(i), att_dir, max_size_mb)
except Exception:
continue
def generate_report(self) -> str:
"""Generate comprehensive PST forensic analysis report."""
root = self.pst.get_root_folder()
self.process_folder(root)
report = {
"analysis_timestamp": datetime.now().isoformat(),
"pst_file": self.pst_path,
"pst_size_bytes": os.path.getsize(self.pst_path),
"statistics": dict(self.stats),
"messages": self.messages[:500],
"attachments": self.attachments[:200],
}
report_path = os.path.join(self.output_dir, "pst_forensic_report.json")
with open(report_path, "w") as f:
json.dump(report, f, indent=2, default=str)
print(f"[*] Total messages: {self.stats['total_messages']}")
print(f"[*] Total attachments: {self.stats['total_attachments']}")
print(f"[*] Parse errors: {self.stats['parse_errors']}")
return report_path
def close(self):
self.pst.close()
def main():
if len(sys.argv) < 3:
print("Usage: python process.py <pst_file> <output_dir>")
sys.exit(1)
analyzer = PSTForensicAnalyzer(sys.argv[1], sys.argv[2])
analyzer.generate_report()
analyzer.close()
if __name__ == "__main__":
main()
Email Header Analysis
Key headers for forensic investigation:
| Header | Forensic Value |
|---|---|
| Received | Message routing chain (read bottom to top) |
| X-Originating-IP | Sender's actual IP address |
| Message-ID | Unique identifier for correlation |
| Date | Send timestamp |
| Return-Path | Bounce address (may differ from From) |
| DKIM-Signature | Domain authentication signature |
| Authentication-Results | SPF, DKIM, DMARC verification results |
| X-Mailer | Email client used |
References
- MailXaminer PST Forensics: https://www.mailxaminer.com/blog/outlook-pst-file-forensics/
- libpff Documentation: https://github.com/libyal/libpff
- PST File Format Specification: https://docs.microsoft.com/en-us/openspecs/office_file_formats/ms-pst/
- SANS Email Forensics: https://www.sans.org/blog/email-forensics/
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