What it does. Parse and analyze email headers (Received chain, Return-Path, Message-ID) Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation, or copy the skill folder into ~/.claude/skills/analyzing-email-headers-for-phishing-investigation/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md

SKILL.md (verbatim)

1 placeholder credential was shortened (for example to api_key=YOUR_KEY) to pass the site's secret filter.

name: analyzing-email-headers-for-phishing-investigation
description: Parse and analyze email headers (Received chain, Return-Path, Message-ID)
  to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC
  results to confirm or rule out sender spoofing. Use when triaging a suspicious or
  reported email, investigating a phishing incident, or verifying whether a message's
  sender domain was spoofed.
domain: cybersecurity
subdomain: digital-forensics
tags:
- forensics
- email-analysis
- phishing
- spf
- dkim
- dmarc
- header-analysis
version: '1.0'
author: mahipal
license: Apache-2.0
atlas_techniques:
- AML.T0052
nist_csf:
- RS.AN-03
- DE.AE-02
- RS.MA-01
mitre_attack:
- T1566.001
- T1566.002
- T1598.003
mitre_f3:
  version: '1.1'
  tactics:
  - reconnaissance
  - initial-access
  - stealth
  - resource-development
  techniques:
  - id: T1598
    name: Phishing for Information
    tactic: reconnaissance
    source: attack
  - id: T1660
    name: Phishing
    tactic: initial-access
    source: attack
  - id: T1672
    name: Email Spoofing
    tactic: stealth
    source: attack
  - id: F1032
    name: Impersonate Official
    tactic: initial-access
    source: f3
  - id: T1583.001
    name: 'Acquire Infrastructure: Domains'
    tactic: resource-development
    source: attack
  - id: F1020.002
    name: 'Create Fake Materials: Fake Website'
    tactic: resource-development
    source: f3

Analyzing Email Headers for Phishing Investigation

When to Use

• When investigating a suspected phishing email to determine its true origin
• For verifying sender authenticity and detecting email spoofing
• During incident response when a user has clicked a phishing link
• When tracing the delivery path and relay servers of a suspicious email
• For validating SPF, DKIM, and DMARC alignment to identify forgery

Prerequisites

• Raw email headers from the suspicious message (EML or MSG format)
• Understanding of SMTP protocol and email header fields
• Access to DNS lookup tools (dig, nslookup) for SPF/DKIM/DMARC verification
• Email header analysis tools (MHA, emailheaders.net concepts)
• Python with email parsing libraries for automated analysis
• Access to threat intelligence platforms for IP/domain reputation

Workflow

Step 1: Extract Raw Email Headers

# Export from Outlook: Open email > File > Properties > Internet Headers
# Export from Gmail: Open email > Three dots > Show original
# Export from Thunderbird: View > Message Source

# If working with EML file from forensic image
cp /mnt/evidence/Users/suspect/AppData/Local/Microsoft/Outlook/phishing_email.eml \
   /cases/case-2024-001/email/

# If working with PST file, extract individual messages
pip install pypff
python3 << 'PYEOF'
import pypff

pst = pypff.file()
pst.open("/cases/case-2024-001/email/outlook.pst")
root = pst.get_root_folder()

def extract_messages(folder, path=""):
    for i in range(folder.get_number_of_sub_messages()):
        msg = folder.get_sub_message(i)
        headers = msg.get_transport_headers()
        subject = msg.get_subject()
        if headers:
            filename = f"/cases/case-2024-001/email/msg_{i}_{subject[:30]}.txt"
            with open(filename, 'w') as f:
                f.write(headers)
    for i in range(folder.get_number_of_sub_folders()):
        extract_messages(folder.get_sub_folder(i))

extract_messages(root)
PYEOF

Step 2: Parse the Email Header Chain

# Parse headers using Python email library
python3 << 'PYEOF'
import email
from email import policy

with open('/cases/case-2024-001/email/phishing_email.eml', 'r') as f:
    msg = email.message_from_file(f, policy=policy.default)

print("=== KEY HEADER FIELDS ===")
print(f"From:          {msg['From']}")
print(f"To:            {msg['To']}")
print(f"Subject:       {msg['Subject']}")
print(f"Date:          {msg['Date']}")
print(f"Message-ID:    {msg['Message-ID']}")
print(f"Reply-To:      {msg['Reply-To']}")
print(f"Return-Path:   {msg['Return-Path']}")
print(f"X-Mailer:      {msg['X-Mailer']}")
print(f"X-Originating-IP: {msg['X-Originating-IP']}")

print("\n=== RECEIVED HEADERS (bottom-up = chronological) ===")
received_headers = msg.get_all('Received')
if received_headers:
    for i, header in enumerate(reversed(received_headers)):
        print(f"\nHop {i+1}: {header.strip()}")

print("\n=== AUTHENTICATION RESULTS ===")
auth_results = msg.get_all('Authentication-Results')
if auth_results:
    for result in auth_results:
        print(result)

print(f"\nARC-Authentication-Results: {msg.get('ARC-Authentication-Results', 'Not present')}")
print(f"Received-SPF: {msg.get('Received-SPF', 'Not present')}")
print(f"DKIM-Signature: {msg.get('DKIM-Signature', 'Not present')}")
PYEOF

Step 3: Validate SPF, DKIM, and DMARC Records

# Extract the envelope sender domain
SENDER_DOMAIN="example-corp.com"

# Check SPF record
dig TXT $SENDER_DOMAIN +short | grep "v=spf1"
# Example: "v=spf1 include:_spf.google.com include:sendgrid.net ~all"

# Check DKIM record (selector from DKIM-Signature header, e.g., "s=selector1")
DKIM_SELECTOR="selector1"
dig TXT ${DKIM_SELECTOR}._domainkey.${SENDER_DOMAIN} +short

# Check DMARC record
dig TXT _dmarc.${SENDER_DOMAIN} +short
# Example: "v=DMARC1; p=reject; rua=mailto:dmarc@example-corp.com; pct=100"

# Verify the sending IP against SPF
# Extract IP from first Received header
SENDING_IP="203.0.113.45"

# Manual SPF check using python
python3 << 'PYEOF'
import spf  # pip install pyspf

result, explanation = spf.check2(
    i='203.0.113.45',
    s='sender@example-corp.com',
    h='mail.example-corp.com'
)
print(f"SPF Result: {result}")
print(f"Explanation: {explanation}")
# Results: pass, fail, softfail, neutral, none, temperror, permerror
PYEOF

# Check if sending IP is in known malicious IP lists
# Query AbuseIPDB or VirusTotal
curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \
   -H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool

Step 4: Analyze Sender Domain and Infrastructure

# WHOIS lookup on sender domain
whois $SENDER_DOMAIN | grep -iE '(registrar|creation|expiration|registrant|nameserver)'

# Check domain age (recently registered domains are suspicious)
# DNS record investigation
dig A $SENDER_DOMAIN +short
dig MX $SENDER_DOMAIN +short
dig NS $SENDER_DOMAIN +short

# Reverse DNS on sending IP
dig -x $SENDING_IP +short

# Check for lookalike/typosquatting domains
# Compare with legitimate domain using visual similarity
python3 << 'PYEOF'
import Levenshtein  # pip install python-Levenshtein

legitimate = "microsoft.com"
suspicious = "micr0soft.com"

distance = Levenshtein.distance(legitimate, suspicious)
ratio = Levenshtein.ratio(legitimate, suspicious)
print(f"Edit distance: {distance}")
print(f"Similarity ratio: {ratio:.2%}")
if ratio > 0.8:
    print("WARNING: Likely typosquatting/lookalike domain!")
PYEOF

# Check domain reputation on VirusTotal
curl -s "https://www.virustotal.com/api/v3/domains/${SENDER_DOMAIN}" \
   -H "x-apikey: YOUR_KEY | python3 -m json.tool

# Check if the Reply-To differs from From (common phishing indicator)
python3 -c "
import email
with open('/cases/case-2024-001/email/phishing_email.eml') as f:
    msg = email.message_from_file(f)
from_addr = email.utils.parseaddr(msg['From'])[1]
reply_to = email.utils.parseaddr(msg.get('Reply-To', msg['From']))[1]
if from_addr != reply_to:
    print(f'WARNING: From ({from_addr}) != Reply-To ({reply_to})')
else:
    print('From and Reply-To match')
"

Step 5: Examine Email Body and Attachments

# Extract URLs from email body
python3 << 'PYEOF'
import email
import re
from email import policy

with open('/cases/case-2024-001/email/phishing_email.eml', 'r') as f:
    msg = email.message_from_file(f, policy=policy.default)

body = msg.get_body(preferencelist=('html', 'plain'))
if body:
    content = body.get_content()
    urls = re.findall(r'https?://[^\s<>"\']+', content)
    print("=== URLs FOUND IN EMAIL BODY ===")
    for url in set(urls):
        print(f"  {url}")

    # Check for URL obfuscation (display text != href)
    href_pattern = re.findall(r'<a[^>]*href=["\']([^"\']+)["\'][^>]*>(.*?)</a>', content, re.DOTALL)
    print("\n=== HYPERLINK ANALYSIS ===")
    for href, text in href_pattern:
        display_url = re.findall(r'https?://[^\s<]+', text)
        if display_url and display_url[0] != href:
            print(f"  MISMATCH: Display='{display_url[0]}' -> Actual='{href}'")

# Extract and hash attachments
print("\n=== ATTACHMENTS ===")
for part in msg.walk():
    if part.get_content_disposition() == 'attachment':
        filename = part.get_filename()
        content = part.get_payload(decode=True)
        import hashlib
        sha256 = hashlib.sha256(content).hexdigest()
        print(f"  File: {filename}, Size: {len(content)}, SHA-256: {sha256}")
        with open(f'/cases/case-2024-001/email/attachments/{filename}', 'wb') as af:
            af.write(content)
PYEOF

# Submit attachment hashes to VirusTotal
# Submit URLs to URLhaus or PhishTank for reputation check

Key Concepts

Tools & Systems

Common Scenarios

Scenario 1: CEO Fraud / Business Email Compromise The email claims to be from the CEO but Reply-To points to a Gmail address, SPF fails because the sending IP is not authorized for the spoofed domain, DKIM is missing, and the From domain is a lookalike (ceo-company.com vs company.com).

Scenario 2: Credential Harvesting Phishing Email contains a link that displays "login.microsoft.com" but href points to a lookalike domain, the attachment is an HTML file containing a fake login page with credential exfiltration JavaScript, the sending domain was registered 3 days ago.

Scenario 3: Malware Delivery via Attachment Email with an Office document attachment containing macros, the sender domain passes SPF but the account was compromised, DKIM signature is valid (sent from legitimate infrastructure), attachment SHA-256 matches known malware on VirusTotal.

Scenario 4: Spear Phishing with Legitimate Service Attacker uses a legitimate email marketing service to send phishing, SPF and DKIM pass because the service is authorized, the phishing is in the content not the infrastructure, requires URL and content analysis rather than header authentication checks.

Output Format

Email Header Analysis Report:
  Subject:     "Urgent: Invoice Payment Required"
  From:        accounting@examp1e-corp.com (SPOOFED)
  Reply-To:    payments.urgent@gmail.com (MISMATCH)
  Return-Path: <bounce@mail-server.xyz>
  Date:        2024-01-15 09:23:45 UTC

  Delivery Path (4 hops):
    Hop 1: mail-server.xyz [203.0.113.45] -> relay1.isp.com
    Hop 2: relay1.isp.com -> mx.target-company.com
    Hop 3: mx.target-company.com -> internal-filter.target.com
    Hop 4: internal-filter.target.com -> mailbox

  Authentication:
    SPF:    FAIL (203.0.113.45 not authorized for examp1e-corp.com)
    DKIM:   NONE (no signature present)
    DMARC:  FAIL (p=none, no enforcement)

  Indicators of Phishing:
    - Lookalike domain (examp1e-corp.com vs example-corp.com, 96% similar)
    - From/Reply-To mismatch
    - Domain registered 2 days before email sent
    - URL in body points to credential harvesting page
    - Attachment: invoice.xlsm (SHA-256: a3f2...) - Known malware on VT

  Risk Level: HIGH

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Email Header Analysis Tools

Python email Module

Parsing EML Files

import email
from email import policy

with open("phishing.eml", "r") as f:
    msg = email.message_from_file(f, policy=policy.default)

msg["From"]           # From header
msg["To"]             # To header
msg["Subject"]        # Subject line
msg["Message-ID"]     # Unique message identifier
msg["Reply-To"]       # Reply-To address
msg["Return-Path"]    # Envelope sender
msg.get_all("Received")  # All Received headers (list)
msg.get_all("Authentication-Results")  # Auth results

Body and Attachment Extraction

body = msg.get_body(preferencelist=("html", "plain"))
content = body.get_content()

for part in msg.walk():
    if part.get_content_disposition() == "attachment":
        filename = part.get_filename()
        data = part.get_payload(decode=True)

dig - DNS Record Lookup

SPF Record

dig TXT example.com +short
# Output: "v=spf1 include:_spf.google.com ~all"

DKIM Record

dig TXT selector1._domainkey.example.com +short

DMARC Record

dig TXT _dmarc.example.com +short
# Output: "v=DMARC1; p=reject; rua=mailto:dmarc@example.com"

pyspf - SPF Validation (Python)

Syntax

import spf
result, explanation = spf.check2(
    i="203.0.113.45",            # Sending IP
    s="sender@example.com",       # Envelope sender
    h="mail.example.com"          # HELO hostname
)
# Results: pass, fail, softfail, neutral, none, temperror, permerror

dkimpy - DKIM Verification (Python)

Syntax

import dkim
with open("email.eml", "rb") as f:
    message = f.read()
result = dkim.verify(message)
# Returns True/False

AbuseIPDB - IP Reputation

API Endpoint

curl -G "https://api.abuseipdb.com/api/v2/check" \
  -H "Key: YOUR_API_KEY" \
  -H "Accept: application/json" \
  -d "ipAddress=203.0.113.45" -d "maxAgeInDays=90"

Response Fields

VirusTotal - Domain/URL Reputation

Domain Lookup

curl -H "x-apikey: YOUR_KEY" \
  "https://www.virustotal.com/api/v3/domains/suspicious.com"

URL Scan

curl -X POST "https://www.virustotal.com/api/v3/urls" \
  -H "x-apikey: YOUR_KEY" \
  -d "url=http://suspicious-url.com/login"

whois - Domain Registration

Syntax

whois suspicious-domain.com

Key Fields

• Registrar - Domain registrar
• Creation Date - When domain was registered
• Registrant - Domain owner info
• Name Server - Authoritative DNS servers

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