security analyst security skill risk: medium
Kerberos Pass-the-Ticket Attack Detector
The prompt provides steps to detect Kerberos Pass-the-Ticket attacks by enabling audit logging, forwarding Event IDs 4768/4769/4771, building correlation rules for anomalies such a…
SKILL 4 files · 2 folders
SKILL.md
--- name: detecting-pass-the-ticket-attacks description: "Detect Kerberos Pass-the-Ticket (PtT) attacks by analyzing Windows Event IDs 4768, 4769, and 4771 for anomalous" --- # Detecting Pass-the-Ticket Attacks ## Overview Pass-the-Ticket (PtT) is a credential theft technique (MITRE ATT&CK T1550.003) where adversaries steal Kerberos tickets (TGT or TGS) from one system and replay them on another to authenticate without knowing the user's password. This skill teaches detection of PtT attacks by correlating Windows Security Event IDs 4768 (TGT request), 4769 (TGS request), and 4771 (pre-authentication failure) for anomalies such as ticket reuse across different hosts, RC4 encryption downgrades, and unusual service ticket request volumes. ## When to Use - When investigating security incidents that require detecting pass the ticket attacks - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Windows Domain Controller with advanced audit policy enabled (Audit Kerberos Authentication Service, Audit Kerberos Service Ticket Operations) - Splunk or Elastic SIEM ingesting Windows Security event logs - Sysmon deployed on endpoints for supplementary process telemetry - Python 3.8+ with `requests` library ## Steps 1. Enable Kerberos audit logging on Domain Controllers via Group Policy 2. Forward Event IDs 4768, 4769, and 4771 to SIEM platform 3. Deploy detection rules for RC4 encryption downgrade (TicketEncryptionType 0x17) 4. Create correlation rule for ticket reuse across multiple source IPs 5. Build baseline of normal TGS request volume per user/host 6. Alert on standard deviation anomalies in ticket request patterns 7. Investigate flagged events with enrichment from Active Directory ## Expected Output JSON report containing detected PtT indicators including anomalous ticket requests, RC4 downgrades, cross-host ticket reuse events, and risk-scored users with MITRE ATT&CK technique mapping.
REQUIRED CONTEXT
- Windows Security event logs containing Event IDs 4768, 4769, 4771
- SIEM platform (Splunk or Elastic)
OPTIONAL CONTEXT
- Sysmon process telemetry
- Active Directory enrichment data
EXPECTED OUTPUT
- Format
- json
- Schema
- json · detected PtT indicators, anomalous ticket requests, RC4 downgrades, cross-host ticket reuse events, risk-scored users, MITRE ATT&CK technique mapping
- Constraints
-
- include anomalous ticket requests
- include RC4 downgrades
- include cross-host ticket reuse events
- include risk-scored users
- include MITRE ATT&CK technique mapping
SUCCESS CRITERIA
- Detect Kerberos PtT attacks via Event IDs 4768/4769/4771
- Identify RC4 downgrades and cross-host reuse
- Produce risk-scored JSON report with ATT&CK mapping
CAVEATS
- Dependencies
-
- Windows Domain Controller with advanced audit policy enabled
- Splunk or Elastic SIEM ingesting Windows Security logs
- Sysmon on endpoints
- Python 3.8+ with requests library
- Missing context
-
- Sample SIEM queries or detection rules
- Exact anomaly thresholds or baselines
- Full JSON schema for expected output
- Ambiguities
-
- Description sentence is truncated mid-phrase ('for anomalous')
QUALITY
- OVERALL
- 0.68
- CLARITY
- 0.75
- SPECIFICITY
- 0.55
- REUSABILITY
- 0.80
- COMPLETENESS
- 0.60
IMPROVEMENT SUGGESTIONS
- Complete the truncated description sentence in the YAML frontmatter.
- Add concrete example queries for Splunk or Elastic under the Steps section.
- Define the risk-scoring method and JSON fields explicitly in Expected Output.
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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