analyzing-email-headers-for-phishing-investigation▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.
| name | analyzing-email-headers-for-phishing-investigation |
| description | Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation. |
| 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-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
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.${SENDER_DOMAIN} +short
# Example: "v=DMARC1; p=reject; rua=mailto:[email protected]; pct=100"
# Verify the sending IP against SPF
# Extract IP from first Received header
SENDING_IP="203.0.113.45"
# Manual SPF check using python
python3 << 'PYEOF'
import spf # pip install pyspf
result, explanation = spf.check2(
i='203.0.113.45',
s='[email protected]',
h='mail.example-corp.com'
)
print(f"SPF Result: {result}")
print(f"Explanation: {explanation}")
# Results: pass, fail, softfail, neutral, none, temperror, permerror
PYEOF
# Check if sending IP is in known malicious IP lists
# Query AbuseIPDB or VirusTotal
curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \
-H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool
Step 4: Analyze Sender Domain and Infrastructure
# 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
# 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
How to use analyzing-email-headers-for-phishing-investigation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add analyzing-email-headers-for-phishing-investigation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches analyzing-email-headers-for-phishing-investigation from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate analyzing-email-headers-for-phishing-investigation. Access the skill through slash commands (e.g., /analyzing-email-headers-for-phishing-investigation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
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Ratings
4.6★★★★★64 reviews- ★★★★★Kwame Abebe· Dec 28, 2024
Registry listing for analyzing-email-headers-for-phishing-investigation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hiroshi Menon· Dec 24, 2024
analyzing-email-headers-for-phishing-investigation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ishan Jain· Dec 16, 2024
We added analyzing-email-headers-for-phishing-investigation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Xiao Martin· Dec 16, 2024
Keeps context tight: analyzing-email-headers-for-phishing-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Valentina Singh· Dec 12, 2024
Useful defaults in analyzing-email-headers-for-phishing-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hiroshi Khan· Dec 12, 2024
analyzing-email-headers-for-phishing-investigation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Dec 8, 2024
Registry listing for analyzing-email-headers-for-phishing-investigation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Nov 27, 2024
Keeps context tight: analyzing-email-headers-for-phishing-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hiroshi Haddad· Nov 19, 2024
Keeps context tight: analyzing-email-headers-for-phishing-investigation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ama Iyer· Nov 15, 2024
analyzing-email-headers-for-phishing-investigation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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