detecting-kerberoasting-attacks

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Use when hunting for MITRE T1558 credential access activity or investigating suspected service account password cracking attempts in Active Directory Kerberos logs.

First seen Mar 17, 2026

Installation

$ npx skills add mukul975/anthropic-cybersecurity-skills --skill detecting-kerberoasting-attacks

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 32.4K

License LICENSE

Default branch main

Open issues 20

Status Active

Package contents

Files included with this skill beyond the listing page.

skill md SKILL.md 3,653 B

docs SUMMARY.md 404 B

History

Mar 17, 2026 First seen on skills.sh

Sep 9, 2026 First recorded snapshot · 78 installs

SKILL.md

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

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]