detecting-kerberoasting-attacks
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
Best use case
detecting-kerberoasting-attacks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
Teams using detecting-kerberoasting-attacks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/detecting-kerberoasting-attacks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How detecting-kerberoasting-attacks Compares
| Feature / Agent | detecting-kerberoasting-attacks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Detecting Kerberoasting Attacks ## When to Use - When proactively hunting for indicators of detecting kerberoasting attacks in the environment - After threat intelligence indicates active campaigns using these techniques - During incident response to scope compromise related to these techniques - When EDR or SIEM alerts trigger on related indicators - During periodic security assessments and purple team exercises ## Prerequisites - EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne) - SIEM with relevant log data ingested (Splunk, Elastic, Sentinel) - Sysmon deployed with comprehensive configuration - Windows Security Event Log forwarding enabled - Threat intelligence feeds for IOC correlation ## Workflow 1. **Formulate Hypothesis**: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis. 2. **Identify Data Sources**: Determine which logs and telemetry are needed to validate or refute the hypothesis. 3. **Execute Queries**: Run detection queries against SIEM and EDR platforms to collect relevant events. 4. **Analyze Results**: Examine query results for anomalies, correlating across multiple data sources. 5. **Validate Findings**: Distinguish true positives from false positives through contextual analysis. 6. **Correlate Activity**: Link findings to broader attack chains and threat actor TTPs. 7. **Document and Report**: Record findings, update detection rules, and recommend response actions. ## Key Concepts | Concept | Description | |---------|-------------| | T1558.003 | Kerberoasting | | T1558.004 | AS-REP Roasting | | T1558.001 | Golden Ticket | ## Tools & Systems | Tool | Purpose | |------|---------| | CrowdStrike Falcon | EDR telemetry and threat detection | | Microsoft Defender for Endpoint | Advanced hunting with KQL | | Splunk Enterprise | SIEM log analysis with SPL queries | | Elastic Security | Detection rules and investigation timeline | | Sysmon | Detailed Windows event monitoring | | Velociraptor | Endpoint artifact collection and hunting | | Sigma Rules | Cross-platform detection rule format | ## Common Scenarios 1. **Scenario 1**: Rubeus kerberoast targeting all SPN accounts 2. **Scenario 2**: GetUserSPNs.py from Impacket requesting RC4 tickets 3. **Scenario 3**: Targeted kerberoast against high-privilege service accounts 4. **Scenario 4**: AS-REP roasting accounts without pre-authentication ## Output Format ``` Hunt ID: TH-DETECT-[DATE]-[SEQ] Technique: T1558.003 Host: [Hostname] User: [Account context] Evidence: [Log entries, process trees, network data] Risk Level: [Critical/High/Medium/Low] Confidence: [High/Medium/Low] Recommended Action: [Containment, investigation, monitoring] ```
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