Investigating Phishing Email Incident

When to Use

Use this skill when:

• A user reports a suspicious email via the phishing report button or helpdesk ticket
• Email security gateway flags a message that bypassed initial filters
• Automated detection identifies credential harvesting URLs or malicious attachments
• A phishing campaign targeting the organization requires scope assessment

Do not use for spam or marketing emails without malicious intent — route those to email administration for filter tuning.

Prerequisites

• Access to email gateway logs (Proofpoint, Mimecast, or Microsoft Defender for Office 365)
• Splunk or SIEM with email log ingestion (O365 Message Trace, Exchange tracking logs)
• Sandbox access (Any.Run, Joe Sandbox, or Hybrid Analysis) for URL/attachment detonation
• Microsoft Graph API or Exchange Admin Center for email search and purge operations
• URLScan.io and VirusTotal API keys

Workflow

Step 1: Extract and Analyze Email Headers

Obtain the full email headers (.eml file) from the reported message:

import email
from email import policy

with open("phishing_sample.eml", "rb") as f:
    msg = email.message_from_binary_file(f, policy=policy.default)

# Extract key headers
print(f"From: {msg['From']}")
print(f"Return-Path: {msg['Return-Path']}")
print(f"Reply-To: {msg['Reply-To']}")
print(f"Subject: {msg['Subject']}")
print(f"Message-ID: {msg['Message-ID']}")
print(f"X-Originating-IP: {msg['X-Originating-IP']}")

# Parse Received headers (bottom-up for true origin)
for header in reversed(msg.get_all('Received', [])):
    print(f"Received: {header[:120]}")

# Check authentication results
print(f"Authentication-Results: {msg['Authentication-Results']}")
print(f"DKIM-Signature: {msg.get('DKIM-Signature', 'NONE')[:80]}")

Key checks:

• SPF: Does Return-Path domain match sending IP? Look for spf=pass or spf=fail
• DKIM: Is the signature valid? dkim=pass confirms the email was not modified in transit
• DMARC: Does the From domain align with SPF/DKIM domains? dmarc=fail indicates spoofing

Step 2: Analyze URLs and Attachments

URL Analysis:

import requests

# Submit URL to URLScan.io
url_to_scan = "https://evil-login.example.com/office365"
response = requests.post(
    "https://urlscan.io/api/v1/scan/",
    headers={"API-Key": "YOUR_KEY", "Content-Type": "application/json"},
    json={"url": url_to_scan, "visibility": "unlisted"}
)
scan_id = response.json()["uuid"]
print(f"Scan URL: https://urlscan.io/result/{scan_id}/")

# Check VirusTotal for URL reputation
import vt
client = vt.Client("YOUR_VT_API_KEY")
url_id = vt.url_id(url_to_scan)
url_obj = client.get_object(f"/urls/{url_id}")
print(f"VT Score: {url_obj.last_analysis_stats}")
client.close()

Attachment Analysis:

import hashlib

# Calculate file hashes
with open("attachment.docx", "rb") as f:
    content = f.read()
    md5 = hashlib.md5(content).hexdigest()
    sha256 = hashlib.sha256(content).hexdigest()

print(f"MD5: {md5}")
print(f"SHA256: {sha256}")

# Submit to MalwareBazaar for lookup
response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256}
)
print(response.json()["query_status"])

Submit to sandbox (Any.Run or Joe Sandbox) for dynamic analysis of macros, PowerShell execution, and C2 callbacks.

Step 3: Determine Campaign Scope

Search for all recipients of the same phishing email in Splunk:

index=email sourcetype="o365:messageTrace"
(SenderAddress="attacker@evil-domain.com" OR Subject="Urgent: Password Reset Required"
 OR MessageId="<phishing-message-id@evil.com>")
earliest=-7d
| stats count by RecipientAddress, DeliveryStatus, MessageTraceId
| sort - count

Alternatively, use Microsoft Graph API:

import requests

headers = {"Authorization": f"Bearer {access_token}"}
params = {
    "$filter": f"subject eq 'Urgent: Password Reset Required' and "
               f"receivedDateTime ge 2024-03-14T00:00:00Z",
    "$select": "sender,toRecipients,subject,receivedDateTime",
    "$top": 100
}
response = requests.get(
    "https://graph.microsoft.com/v1.0/users/admin@company.com/messages",
    headers=headers, params=params
)
messages = response.json()["value"]
print(f"Found {len(messages)} matching messages")

Step 4: Identify Impacted Users (Who Clicked)

Check proxy/web logs for users who visited the phishing URL:

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