detecting-kerberoasting-attacks skill (Anthropic-Cybersecurity-Skills)

From Public Agent Wiki

What it does. Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/detecting-kerberoasting-attacks/SKILL.md
License Apache-2.0 (skill folder LICENSE)
Author mukul975
Fetched 2026-09-10

Install

  • npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-kerberoasting-attacks, or copy the skill folder into ~/.claude/skills/detecting-kerberoasting-attacks/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/detecting-kerberoasting-attacks/SKILL.md

SKILL.md (verbatim)

name: detecting-kerberoasting-attacks
description: 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.
domain: cybersecurity
subdomain: threat-hunting
tags:
- threat-hunting
- mitre-attack
- kerberoasting
- credential-access
- kerberos
- t1558
- proactive-detection
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Application Protocol Command Analysis
- Network Isolation
- Network Traffic Analysis
- Client-server Payload Profiling
- Network Traffic Community Deviation
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1046
- T1057
- T1082
- T1083
- T1003

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]

Other files in this skill

assets/template.md (verbatim)

Detecting Kerberoasting Attacks - Hunt Template

Hunt Metadata

Field Value
Hunt ID TH-DETECT-YYYY-MM-DD-NNN
Analyst
Date Started
Date Completed
Status [ ] In Progress / [ ] Complete
Priority [ ] Critical / [ ] High / [ ] Medium / [ ] Low

Hypothesis

Statement: [Formulate a clear, testable hypothesis]

Basis: [ ] Threat Intel / [ ] ATT&CK Gap / [ ] Anomaly / [ ] Incident Follow-up

Target Techniques

  • T1558.003 - Kerberoasting
  • T1558.004 - AS-REP Roasting
  • T1558.001 - Golden Ticket

Data Sources

  • Sysmon Event Logs
  • Windows Security Event Logs
  • EDR Telemetry (Platform: _____________)
  • SIEM (Platform: _____________)
  • Network Logs (Proxy/Firewall/DNS)
  • Cloud Audit Logs
  • Email Gateway Logs
  • Application Logs

Queries Executed

Query 1: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Query 2: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Findings

# Timestamp Host User Technique Evidence Summary Risk Verdict
1 TP / FP / BTP
2 TP / FP / BTP
3 TP / FP / BTP

IOCs Discovered

Network IOCs

Type Value Context Confidence
IP
Domain
URL

Host IOCs

Type Value Context Confidence
SHA256
Filename
Registry Key
Scheduled Task

Hunt Results Summary

Metric Count
Total Events Analyzed
Anomalies Identified
True Positives
False Positives
Benign True Positives
New IOCs Discovered
Detection Rules Created
Detection Rules Updated

Hypothesis Outcome

  • Confirmed: Evidence supports the hypothesis
  • Partially Confirmed: Some evidence found, further investigation needed
  • Refuted: No evidence found
  • Inconclusive: Insufficient data

Recommendations

  1. Immediate Actions: [Containment, remediation steps]
  2. Detection Improvements: [New rules, tuning recommendations]
  3. Visibility Gaps: [Missing data sources, coverage needs]
  4. Security Hardening: [Configuration changes, policy updates]
  5. Follow-up Hunts: [Related hypotheses to investigate]

Analyst Notes

[Free-form notes, observations, and lessons learned]

references/api-reference.md (verbatim)

API Reference: Detecting Kerberoasting Attacks

python-evtx Library

from Evtx.Evtx import FileHeader
with open("Security.evtx", "rb") as f:
    fh = FileHeader(f)
    for record in fh.records():
        xml_string = record.xml()

Event ID 4769 - Kerberos TGS Request

<EventData>
  <Data Name="TargetUserName">svc_sql</Data>
  <Data Name="ServiceName">MSSQLSvc/db01.corp.local:1433</Data>
  <Data Name="TicketEncryptionType">0x17</Data>
  <Data Name="TicketOptions">0x40810000</Data>
  <Data Name="IpAddress">::ffff:10.0.0.50</Data>
  <Data Name="Status">0x0</Data>
</EventData>

Encryption Type Values

Hex Type Risk
0x17 RC4-HMAC Kerberoasting indicator
0x18 RC4-HMAC-EXP Kerberoasting indicator
0x11 AES128-CTS-HMAC-SHA1 Normal
0x12 AES256-CTS-HMAC-SHA1 Normal

Detection Logic

  1. Filter Event 4769 where TicketEncryptionType = 0x17 (RC4)
  2. Exclude machine accounts (ServiceName ending in $)
  3. Exclude krbtgt service
  4. Alert on high-volume TGS from single source (>10 unique SPNs in 5 min)
  5. Correlate with Event 4624 for source attribution

Event ID 4624 - Logon Event (Correlation)

<Data Name="TargetUserName">attacker_user</Data>
<Data Name="LogonType">3</Data>
<Data Name="IpAddress">10.0.0.50</Data>
<Data Name="WorkstationName">WORKSTATION1</Data>

MITRE ATT&CK Mapping

  • T1558.003 - Kerberoasting
  • T1558 - Steal or Forge Kerberos Tickets

references/standards.md (verbatim)

Standards and References - Detecting Kerberoasting Attacks

MITRE ATT&CK Mappings

Technique Name Description
T1558.003 Kerberoasting See attack.mitre.org/techniques/T1558/003
T1558.004 AS-REP Roasting See attack.mitre.org/techniques/T1558/004
T1558.001 Golden Ticket See attack.mitre.org/techniques/T1558/001

Detection Data Sources

Source Event ID Purpose
Sysmon 1 Process creation with command line
Sysmon 3 Network connection initiated
Sysmon 7 Image loaded (DLL)
Sysmon 10 Process access (LSASS)
Sysmon 11 File creation
Sysmon 12/13 Registry create/set
Sysmon 22 DNS query
Sysmon 25 Process tampering
Windows Security 4624 Successful logon
Windows Security 4625 Failed logon
Windows Security 4648 Explicit credential logon
Windows Security 4672 Special privileges assigned
Windows Security 4688 Process creation
Windows Security 4697 Service installed
Windows Security 4698 Scheduled task created
Windows Security 4769 Kerberos TGS requested
Windows Security 5140 Network share accessed

References

references/workflows.md (verbatim)

Detailed Hunting Workflow - Detecting Kerberoasting Attacks

Phase 1: Data Collection and Querying

Splunk SPL Query

index=wineventlog EventCode=4769 Ticket_Encryption_Type=0x17
| where Service_Name!="krbtgt" AND NOT match(Service_Name, "\\$")
| stats count dc(Service_Name) as unique_services by Account_Name Client_Address
| where unique_services > 5
| sort -unique_services

KQL Query (Microsoft Defender for Endpoint)

SecurityEvent
| where EventID == 4769
| where TicketEncryptionType == "0x17"
| where ServiceName !endswith "$" and ServiceName != "krbtgt"
| summarize ServiceCount=dcount(ServiceName), Services=make_set(ServiceName) by SubjectUserName, IpAddress
| where ServiceCount > 5

Phase 2: Baseline and Anomaly Detection

Step 2.1 - Establish Normal Behavior Baseline

  • Collect 30 days of historical data for the targeted technique
  • Document expected patterns, frequencies, and legitimate use cases
  • Identify known false positive sources and document exceptions
  • Build statistical baseline (mean, standard deviation) for key metrics

Step 2.2 - Identify Anomalies

  • Compare current activity against the 30-day baseline
  • Flag events exceeding 3 standard deviations from normal
  • Prioritize anomalies by risk score and potential business impact
  • Cross-reference with threat intelligence for known IOCs

Phase 3: Investigation and Correlation

Step 3.1 - Deep Dive Analysis

  • For each anomaly, collect full process tree context
  • Correlate with network activity, file operations, and authentication events
  • Check binary signatures, file hashes, and certificate validity
  • Review user account context and access patterns

Step 3.2 - Attack Chain Reconstruction

  • Map findings to MITRE ATT&CK kill chain stages
  • Identify initial access vector if applicable
  • Trace lateral movement and privilege escalation paths
  • Determine data access and potential exfiltration

Phase 4: Validation and Response

Step 4.1 - True/False Positive Determination

  • Verify findings with system owners and IT operations
  • Check change management records for authorized activities
  • Validate user context (authorized actions vs. compromised account)
  • Document determination rationale for each finding

Step 4.2 - Response Actions

  • For confirmed threats: initiate incident response procedures
  • For detection gaps: create or update detection rules
  • For false positives: tune existing rules and update exclusions
  • Update threat hunting playbook with lessons learned

Phase 5: Documentation and Reporting

Step 5.1 - Hunt Report

  • Summarize hypothesis, methodology, and findings
  • Include all queries executed and their results
  • Document IOCs discovered and detection rules created
  • Provide recommendations for security improvements

Step 5.2 - Knowledge Base Update

  • Add findings to threat intelligence platform
  • Update MITRE ATT&CK coverage heatmap
  • Share detection rules via Sigma format
  • Schedule follow-up hunts for related techniques

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