external/anthropic-cybersecurity-skills/skills/investigating-phishing-email-incident/SKILL.md
Investigates phishing email incidents from initial user report through header analysis, URL/attachment detonation, impacted user identification, and containment actions using SOC tools like Splunk, Microsoft Defender, and sandbox analysis platforms. Use when a reported phishing email requires full incident investigation to determine scope and impact.
npx skillsauth add seikaikyo/dash-skills investigating-phishing-email-incidentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Do not use for spam or marketing emails without malicious intent — route those to email administration for filter tuning.
Obtain the full email headers (.eml file) from the reported message:
import email
from email import policy
with open("phishing_sample.eml", "rb") as f:
msg = email.message_from_binary_file(f, policy=policy.default)
# Extract key headers
print(f"From: {msg['From']}")
print(f"Return-Path: {msg['Return-Path']}")
print(f"Reply-To: {msg['Reply-To']}")
print(f"Subject: {msg['Subject']}")
print(f"Message-ID: {msg['Message-ID']}")
print(f"X-Originating-IP: {msg['X-Originating-IP']}")
# Parse Received headers (bottom-up for true origin)
for header in reversed(msg.get_all('Received', [])):
print(f"Received: {header[:120]}")
# Check authentication results
print(f"Authentication-Results: {msg['Authentication-Results']}")
print(f"DKIM-Signature: {msg.get('DKIM-Signature', 'NONE')[:80]}")
Key checks:
Return-Path domain match sending IP? Look for spf=pass or spf=faildkim=pass confirms the email was not modified in transitFrom domain align with SPF/DKIM domains? dmarc=fail indicates spoofingURL Analysis:
import requests
# Submit URL to URLScan.io
url_to_scan = "https://evil-login.example.com/office365"
response = requests.post(
"https://urlscan.io/api/v1/scan/",
headers={"API-Key": "YOUR_KEY", "Content-Type": "application/json"},
json={"url": url_to_scan, "visibility": "unlisted"}
)
scan_id = response.json()["uuid"]
print(f"Scan URL: https://urlscan.io/result/{scan_id}/")
# Check VirusTotal for URL reputation
import vt
client = vt.Client("YOUR_VT_API_KEY")
url_id = vt.url_id(url_to_scan)
url_obj = client.get_object(f"/urls/{url_id}")
print(f"VT Score: {url_obj.last_analysis_stats}")
client.close()
Attachment Analysis:
import hashlib
# Calculate file hashes
with open("attachment.docx", "rb") as f:
content = f.read()
md5 = hashlib.md5(content).hexdigest()
sha256 = hashlib.sha256(content).hexdigest()
print(f"MD5: {md5}")
print(f"SHA256: {sha256}")
# Submit to MalwareBazaar for lookup
response = requests.post(
"https://mb-api.abuse.ch/api/v1/",
data={"query": "get_info", "hash": sha256}
)
print(response.json()["query_status"])
Submit to sandbox (Any.Run or Joe Sandbox) for dynamic analysis of macros, PowerShell execution, and C2 callbacks.
Search for all recipients of the same phishing email in Splunk:
index=email sourcetype="o365:messageTrace"
(SenderAddress="[email protected]" OR Subject="Urgent: Password Reset Required"
OR MessageId="<[email protected]>")
earliest=-7d
| stats count by RecipientAddress, DeliveryStatus, MessageTraceId
| sort - count
Alternatively, use Microsoft Graph API:
import requests
headers = {"Authorization": f"Bearer {access_token}"}
params = {
"$filter": f"subject eq 'Urgent: Password Reset Required' and "
f"receivedDateTime ge 2024-03-14T00:00:00Z",
"$select": "sender,toRecipients,subject,receivedDateTime",
"$top": 100
}
response = requests.get(
"https://graph.microsoft.com/v1.0/users/[email protected]/messages",
headers=headers, params=params
)
messages = response.json()["value"]
print(f"Found {len(messages)} matching messages")
Check proxy/web logs for users who visited the phishing URL:
index=proxy dest="evil-login.example.com" earliest=-7d
| stats count, values(action) AS actions, latest(_time) AS last_access
by src_ip, user
| lookup asset_lookup_by_cidr ip AS src_ip OUTPUT owner, category
| sort - count
| table user, src_ip, owner, actions, count, last_access
Check if credentials were submitted (POST requests to phishing domain):
index=proxy dest="evil-login.example.com" http_method=POST earliest=-7d
| stats count by src_ip, user, url, status
Purge emails from all mailboxes:
# Microsoft 365 Compliance Search and Purge
New-ComplianceSearch -Name "Phishing_Purge_2024_0315" `
-ExchangeLocation All `
-ContentMatchQuery '(From:[email protected]) AND (Subject:"Urgent: Password Reset Required")'
Start-ComplianceSearch -Identity "Phishing_Purge_2024_0315"
# After search completes, execute purge
New-ComplianceSearchAction -SearchName "Phishing_Purge_2024_0315" -Purge -PurgeType SoftDelete
Block indicators:
Reset compromised credentials:
# Force password reset for impacted users
$impactedUsers = @("[email protected]", "[email protected]")
foreach ($user in $impactedUsers) {
Set-MsolUserPassword -UserPrincipalName $user -ForceChangePassword $true
Revoke-AzureADUserAllRefreshToken -ObjectId (Get-AzureADUser -ObjectId $user).ObjectId
}
Create incident report with full timeline, IOCs, impacted users, and remediation actions taken.
| makeresults
| eval incident_id="PHI-2024-0315",
reported_time="2024-03-15 09:12:00",
sender="attacker@evil-domain[.]com",
subject="Urgent: Password Reset Required",
url="hxxps://evil-login[.]example[.]com/office365",
recipients_count=47,
clicked_count=5,
credentials_submitted=2,
emails_purged=47,
passwords_reset=2,
domains_blocked=1,
disposition="True Positive - Credential Phishing Campaign"
| table incident_id, reported_time, sender, subject, url, recipients_count,
clicked_count, credentials_submitted, emails_purged, passwords_reset, disposition
| Term | Definition | |------|-----------| | SPF (Sender Policy Framework) | DNS TXT record specifying which mail servers are authorized to send on behalf of a domain | | DKIM | DomainKeys Identified Mail — cryptographic signature proving email content was not altered in transit | | DMARC | Domain-based Message Authentication, Reporting and Conformance — policy combining SPF and DKIM alignment | | Credential Harvesting | Phishing technique using fake login pages to capture username/password combinations | | Business Email Compromise (BEC) | Social engineering attack using compromised or spoofed executive email for financial fraud | | Message Trace | O365/Exchange log showing email routing, delivery status, and filtering actions for forensic analysis |
PHISHING INCIDENT REPORT — PHI-2024-0315
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Reported: 2024-03-15 09:12 UTC by jsmith (Finance)
Sender: attacker@evil-domain[.]com (SPF: FAIL, DKIM: NONE, DMARC: FAIL)
Subject: Urgent: Password Reset Required
Payload: Credential harvesting URL
IOCs:
URL: hxxps://evil-login[.]example[.]com/office365
Domain: evil-login[.]example[.]com (registered 2024-03-14, Namecheap)
IP: 185.234.xx.xx (VT: 12/90 malicious)
Scope:
Recipients: 47 users across Finance and HR departments
Clicked: 5 users visited phishing URL
Submitted: 2 users entered credentials (confirmed via POST in proxy logs)
Containment:
[DONE] 47 emails purged via Compliance Search
[DONE] Domain blocked on proxy and DNS sinkhole
[DONE] 2 user passwords reset, sessions revoked
[DONE] MFA enforced for both compromised accounts
[DONE] Inbox rules audited — no forwarding rules found
Status: RESOLVED — No evidence of lateral movement post-compromise
tools
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tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.