analyzing-email-headers-for-phishing-investigation — Claude Code skill
Parses email headers, traces Received hops, and validates SPF, DKIM, and DMARC to expose phishing sources and sender spoofing.
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a claude-codeWhat analyzing-email-headers-for-phishing-investigation does
The skill guides forensic triage for suspicious emails. It outlines steps to extract raw transport headers from email clients, raw EML files, or Outlook PST archives using the pypff Python library. Header order matters. A Python script reads From, Return-Path, and Message-ID fields, then parses Received headers from bottom to top to trace the originating IP address.
Authentication is verified through DNS queries against SPF, DKIM, and DMARC records using dig or the pyspf module. The skill also computes the Levenshtein distance between sender and target domains to flag lookalike typosquatting domains. Running IP reputation checks requires an external AbuseIPDB API key, so you need network access and credentials ready beforehand.
What it helps with
- trace relay servers bottom-up through Received headers
- validate SPF, DKIM, and DMARC DNS records
- detect typosquatting domains using Levenshtein distance
- extract transport headers from PST and EML archives
Example prompts for the agent
- Analyze phishing_email.eml headers and verify SPF, DKIM, and DMARC status
- Trace the Received relay chain in this suspicious email to find the origin IP
- Check sender domain micr0soft.com against microsoft.com for typosquatting
How to install analyzing-email-headers-for-phishing-investigation
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a claude-code2Manually: put the files in place
~/.claude/skills/analyzing-email-headers-for-phishing-investigation/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/analyzing-email-headers-for-phishing-investigation/
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a codex2Manually: put the files in place
.agents/skills/analyzing-email-headers-for-phishing-investigation/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a cursor2Manually: put the files in place
.cursor/skills/analyzing-email-headers-for-phishing-investigation/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-email-headers-for-phishing-investigation2Manually: put the files in place
.github/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a gemini-cli2Platform CLI
gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills --path skills/analyzing-email-headers-for-phishing-investigationManually: put the files in place
.devin/skills/analyzing-email-headers-for-phishing-investigation/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about analyzing-email-headers-for-phishing-investigation
How do I install analyzing-email-headers-for-phishing-investigation in Claude Code?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a claude-code.
What does this skill do?
It guides phishing investigations by parsing email headers from EML or PST files. It reconstructs the delivery path and checks for sender address spoofing.
What tools and libraries are required for the analysis?
You need dig or nslookup for DNS queries, plus Python packages pypff, pyspf, and python-Levenshtein. IP reputation checks also require an AbuseIPDB API key.
Source text
SKILL.md · Apache-2.0
---
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
```bash
# 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
```bash
# 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
```bash
# 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.${SENDER_DOMAIN} +short
# Example: "v=DMARC1; p=reject; rua=mailto:[email protected]; pct=100"
# Verify the sending IP against SPF
# Extract IP from first Received header
SENDING_IP="203.0.113.45"
# Manual SPF check using python
python3 << 'PYEOF'
import spf # pip install pyspf
result, explanation = spf.check2(
i='203.0.113.45',
s='[email protected]',
h='mail.example-corp.com'
)
print(f"SPF Result: {result}")
print(f"Explanation: {explanation}")
# Results: pass, fail, softfail, neutral, none, temperror, permerror
PYEOF
# Check if sending IP is in known malicious IP lists
# Query AbuseIPDB or VirusTotal
curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \
-H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool
```
### Step 4: Analyze Sender Domain and Infrastructure
```bash
# 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_VT_API_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
```bash
# 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
| Concept | Description |
|---------|-------------|
| SPF (Sender Policy Framework) | DNS record specifying authorized mail servers for a domain |
| DKIM (DomainKeys Identified Mail) | Cryptographic signature verifying email content integrity |
| DMARC | Policy framework combining SPF and DKIM for sender authentication |
| Received headers | Server-added headers showing each hop in the delivery chain (read bottom to top) |
| Return-Path | Envelope sender address used for bounce messages; may differ from From |
| Message-ID | Unique identifier assigned by the originating mail server |
| X-Originating-IP | Original sender IP address (added by some mail services) |
| Header forgery | Attackers can forge From, Reply-To, and other headers but not Received chains |
## Tools & Systems
| Tool | Purpose |
|------|---------|
| MXToolbox | Online email header analyzer and DNS lookup |
| dig/nslookup | DNS record queries for SPF, DKIM, DMARC verification |
| pyspf | Python SPF record validation library |
| dkimpy | Python DKIM signature verification library |
| PhishTool | Specialized phishing email analysis platform |
| VirusTotal | URL and file reputation checking service |
| AbuseIPDB | IP address reputation database |
| whois | Domain registration information lookup |
## 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: [email protected] (SPOOFED)
Reply-To: [email protected] (MISMATCH)
Return-Path: <[email protected]>
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
```
© mahipal · Apache-2.0 · mukul975/Anthropic-Cybersecurity-Skills