# Detecting Golden Ticket Attacks In Kerberos Logs

> Use when detect Golden Ticket attacks in Active Directory by analyzing Kerberos TGT anomalies including mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures in domain controller event logs. Use when detecting golden ticket attacks in active directory by analyzing kerberos.

- Skill: `oyi77/detecting-golden-ticket-attacks-in-kerberos-logs` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/detecting-golden-ticket-attacks-in-kerberos-logs`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/detecting-golden-ticket-attacks-in-kerberos-logs/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/detecting-golden-ticket-attacks-in-kerberos-logs

---


# Detecting Golden Ticket Attacks In Kerberos Logs

## Overview

Cybersecurity skill for detecting golden ticket attacks in kerberos logs. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "detecting golden ticket attacks in kerberos logs"
- "Detect Golden Ticket attacks in Active Directory by analyzing Kerberos TGT anoma"


- 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


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## 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

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Define Detection Scope** — Identify the specific golden ticket attacks in kerberos logs techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. **Collect Baseline Data** — Gather historical logs and establish normal behavior patterns for golden ticket attacks in kerberos logs.
3. **Build Detection Queries** — Write detection rules, Sigma rules, or SIEM queries targeting golden ticket attacks in kerberos logs indicators.
4. **Execute Hunts** — Run queries against the collected data, starting with broad filters and narrowing down.
5. **Triage Results** — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. **Document Findings** — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

## Tools

- **SIEM Platform** — Central log aggregation and query execution
- **Sigma Rules** — Vendor-agnostic detection rule format
- **MITRE ATT&CK Navigator** — Technique mapping and coverage analysis


## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- [ ] All golden ticket attacks in kerberos logs procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |