--- name: analyzing-email-headers-for-phishing-investigation-5c143991bf9d 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.${SENDER_DOMAIN} +short # Example: "v=DMARC1; p=reject; rua=mailto:dmarc@example-corp.com; pct=100" # Verify the sending IP against SPF # Extract IP from first Received header SENDING_IP="203.0.113.45" # Manual SPF check using python python3 << 'PYEOF' import spf # pip install pyspf result, explanation = spf.check2( i='203.0.113.45', s='sender@example-corp.com', h='mail.example-corp.com' ) print(f"SPF Result: {result}") print(f"Explanation: {explanation}") # Results: pass, fail, softfail, neutral, none, temperror, permerror PYEOF # Check if sending IP is in known malicious IP lists # Query AbuseIPDB or VirusTotal curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \ -H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool ``` ### Step 4: Analyze Sender Domain and Infrastructure ```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']*href=["\']([^"\']+)["\'][^>]*>(.*?)', 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: accounting@examp1e-corp.com (SPOOFED) Reply-To: payments.urgent@gmail.com (MISMATCH) Return-Path: 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 ``` --- Source attribution: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md Attribution basis: Source repository record Fields of work: Marketing, Sales, Customer Support Roles: Customer Success Manager, PR & Communications, Support Agent, Technical Support Specialties: API Tools: Email Header Analysis Tools, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8d9435e18abf9ebad58a31fab0a1d5908fdfd0a13a00b34ab720c438a8ec0808