본문으로 건너뛰기
cccskills
무료GitHub에 공개

analyzing-email-headers-for-phishing-investigation

Analyze suspicious email headers to trace delivery routes and assess sender spoofing. Review SPF, DKIM, and DMARC sender authentication results, compare sender and reply addresses, and examine links and attachment hashes for phishing indicators.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

연동 도구GmailOutlook
설치 방법 보기

이럴 때 유용합니다

  • Triaging a suspicious email reported by a user
  • Checking whether a sender domain was spoofed
  • Tracing the delivery route of a phishing email
  • Investigating suspicious links and attachments

포함된 파일(4)

  • SKILL.md12.4 KB
  • LICENSE11.0 KB
  • references/api-reference.md2.7 KB
  • scripts/agent.py8.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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_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

ConceptDescription
SPF (Sender Policy Framework)DNS record specifying authorized mail servers for a domain
DKIM (DomainKeys Identified Mail)Cryptographic signature verifying email content integrity
DMARCPolicy framework combining SPF and DKIM for sender authentication
Received headersServer-added headers showing each hop in the delivery chain (read bottom to top)
Return-PathEnvelope sender address used for bounce messages; may differ from From
Message-IDUnique identifier assigned by the originating mail server
X-Originating-IPOriginal sender IP address (added by some mail services)
Header forgeryAttackers can forge From, Reply-To, and other headers but not Received chains

Tools & Systems

ToolPurpose
MXToolboxOnline email header analyzer and DNS lookup
dig/nslookupDNS record queries for SPF, DKIM, DMARC verification
pyspfPython SPF record validation library
dkimpyPython DKIM signature verification library
PhishToolSpecialized phishing email analysis platform
VirusTotalURL and file reputation checking service
AbuseIPDBIP address reputation database
whoisDomain 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: <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

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing saved credential exposure in authorized tests
  • Examining collected DPAPI files offline
  • Reviewing protected browser and RDP credentials
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing risky permissions identified in BloodHound
  • Validating account takeover without resetting a password
  • Generating directory change records during security exercises
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define the assessment scope for systems handling controlled information
  • Calculate an SPRS score and prioritize unmet security requirements
  • Prepare security plans, remediation milestones, and assessment evidence
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Create disk images for investigations using dd or dcfldd on Linux. The workflow covers write protection, bit-for-bit copying, hash checks to verify integrity, and documentation of the acquisition process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preserving a suspect drive before investigation
  • Creating evidence copies of USB devices or memory cards
  • Verifying that a disk image matches its source
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Review Active Directory permissions with ldap3, a Python library for directory queries. Identify excessive access granted to non-administrative accounts, explain potential privilege escalation paths, and produce a JSON report with affected objects and remediation recommendations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Auditing excessive Active Directory permissions
  • Investigating privilege escalation paths from ordinary accounts
  • Reporting affected objects and permission remediation recommendations
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Triage suspicious APK files without running them
  • Inspect dangerous permission combinations and suspicious API calls
  • Extract network indicators for malware detection rules
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

mukul975의 스킬 모두 보기

이 스킬의 문제 신고