본문으로 건너뛰기
cccskills
무료GitHub에 공개

detecting-golden-ticket-attacks-in-kerberos-logs

Investigate Active Directory authentication logs with Splunk and KQL queries to find signs of Golden Ticket attacks, which use forged Kerberos authentication tickets. Examine encryption mismatches, missing ticket requests, unusual lifetimes, and account inconsistencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Detect Golden Ticket attacks in Active Directory using Splunk and KQL queries against domain controller event logs, looking for Kerberos TGT anomalies such as mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures. Use when hunting for Kerberos ticket forgery or krbtgt-based persistence (MITRE T1558.001) in AD environments.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Hunting for forged tickets after credential theft
  • Investigating encryption mismatches in authentication logs
  • Checking for persistent access after incident response
  • Reviewing service tickets without earlier authentication requests

포함된 파일(4)

  • SKILL.md4.6 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.6 KB
  • scripts/agent.py6.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Detecting Golden Ticket Attacks in Kerberos Logs

When to Use

  • When KRBTGT account hash may have been compromised via DCSync or NTDS.dit extraction
  • When hunting for forged Kerberos tickets used for persistent domain access
  • After incident response reveals credential theft at the domain level
  • When investigating impossible logon patterns (users logging in from multiple locations simultaneously)
  • During post-breach assessment to determine if Golden Tickets are in use

Prerequisites

  • Windows Security Event IDs 4768, 4769, 4771 on domain controllers
  • Kerberos policy configuration knowledge (max ticket lifetime, encryption types)
  • Domain controller audit policy enabling Kerberos Service Ticket Operations
  • SIEM with ability to correlate Kerberos events across multiple DCs

Workflow

  1. Monitor TGT Requests (Event 4768): Track Kerberos authentication service requests. Golden Tickets bypass the AS-REQ/AS-REP exchange entirely, so the absence of 4768 before 4769 is suspicious.
  2. Detect Encryption Type Anomalies: Golden Tickets often use RC4 (0x17) encryption. If your domain enforces AES (0x12), any RC4 TGT is a red flag. Monitor TicketEncryptionType in Event 4769.
  3. Check Ticket Lifetime Anomalies: Default Kerberos TGT lifetime is 10 hours with 7-day renewal. Golden Tickets can be forged with 10-year lifetimes. Detect tickets with durations exceeding policy.
  4. Hunt for Non-Existent SIDs: Golden Tickets can include arbitrary SIDs (including non-existent accounts or groups). Correlate TGS requests against known AD SID inventory.
  5. Detect TGS Without Prior TGT: When a service ticket (4769) appears without a preceding TGT request (4768) from the same IP/account, this may indicate a pre-existing Golden Ticket.
  6. Monitor KRBTGT Password Age: Track when KRBTGT was last reset. If KRBTGT hash hasn't changed since a known compromise, Golden Tickets from that period remain valid.
  7. Validate PAC Signatures: With KB5008380+ and PAC validation enforcement, domain controllers reject forged PACs. Monitor for Kerberos failures indicating PAC validation errors.

Detection Queries

Splunk -- RC4 Encryption in Kerberos TGS

index=wineventlog EventCode=4769
| where TicketEncryptionType="0x17"
| where ServiceName!="krbtgt"
| stats count by TargetUserName ServiceName IpAddress TicketEncryptionType Computer
| where count > 5
| sort -count

Splunk -- TGS Without Prior TGT

index=wineventlog (EventCode=4768 OR EventCode=4769)
| stats earliest(_time) as first_tgt by TargetUserName IpAddress EventCode
| eventstats earliest(eval(if(EventCode=4768, first_tgt, null()))) as tgt_time by TargetUserName IpAddress
| where EventCode=4769 AND (isnull(tgt_time) OR first_tgt < tgt_time)
| table TargetUserName IpAddress first_tgt tgt_time

KQL -- Golden Ticket Indicators

SecurityEvent
| where EventID == 4769
| where TicketEncryptionType == "0x17"
| where ServiceName != "krbtgt"
| summarize Count=count() by TargetUserName, IpAddress, ServiceName
| where Count > 5

Common Scenarios

  1. Post-DCSync Golden Ticket: After extracting KRBTGT hash, attacker forges TGT with Domain Admin SID, valid for months until KRBTGT is rotated twice.
  2. RC4 Downgrade: Golden Ticket forged with RC4 encryption in an AES-only environment, detectable by encryption type mismatch.
  3. Cross-Domain Golden Ticket: Forged inter-realm TGT used to pivot between AD domains/forests.
  4. Persistence After Remediation: Golden Tickets surviving password resets because KRBTGT was only rotated once (both current and previous hashes are valid).

Output Format

Hunt ID: TH-GOLDEN-[DATE]-[SEQ]
Suspected Account: [Account using forged ticket]
Source IP: [Client IP]
Target Service: [SPN accessed]
Encryption Type: [RC4/AES128/AES256]
Anomaly: [No prior TGT/RC4 in AES environment/Extended lifetime]
KRBTGT Last Reset: [Date]
Risk Level: [Critical]

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