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Get Started Free →Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.
.claude/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | — | — |
| case-15 | ✗→✓ | ▲ Improved | — | — |
| case-02 | ✓→✓ | = Same ✓ | — | — |
| case-04 | ✓→✓ | = Same ✓ | — | — |
| case-21 | ✗→✗ | = Same ✗ | — | — |
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
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
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.${SENDER_DOMAIN} +short # Example: "v=DMARC1; p=reject; rua=mailto:dmarc@example-corp.com; pct=100" # Verify the sending IP against SPF # Extract IP from first Received header SENDING_IP="203.0.113.45" # Manual SPF check using python python3 << 'PYEOF' import spf # pip install pyspf result, explanation = spf.check2( i='203.0.113.45', s='sender@example-corp.com', h='mail.example-corp.com' ) print(f"SPF Result: {result}") print(f"Explanation: {explanation}") # Results: pass, fail, softfail, neutral, none, temperror, permerror PYEOF # Check if sending IP is in known malicious IP lists # Query AbuseIPDB or VirusTotal curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \ -H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool
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') "
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
| 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 |
| 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 |
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.
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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.