About detecting-kerberoasting-attacks
A detection skill for security analysts and threat hunters investigating Kerberoasting and related activity. It structures the process from hypothesis formation and telemetry queries through validation, correlation, and reporting.
Key features
- 01Form testable hunting hypotheses
- 02Identify logs and telemetry sources
- 03Query SIEM and EDR data
- 04Correlate activity with attack TTPs
- 05Document findings and response actions
Use cases
- 01Hunt for Kerberoasting based on threat intelligence
- 02Investigate EDR or SIEM alerts
- 03Assess detection coverage in purple-team exercises
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ mukul975/ anthropic-cybersecurity-skills --skill detecting-kerberoasting-attacksAfter installing, type/detecting-kerberoasting-attacksin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
LICENSEassets/template.mdreferences/api-reference.mdstandards.mdworkflows.mdscripts/agent.pyprocess.pyDetails
From the project READMEDetecting 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
- Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
- Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
- Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
- Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
- Validate Findings: Distinguish true positives from false positives through contextual analysis.
- Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
- 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
- Scenario 1: Rubeus kerberoast targeting all SPN accounts
- Scenario 2: GetUserSPNs.py from Impacket requesting RC4 tickets
- Scenario 3: Targeted kerberoast against high-privilege service accounts
- 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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