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detecting-kerberoasting-attacks

detecting-kerberoasting-attacks

github.com/mukul975Security35.2k60 installsUpdated 8w ago
Repository

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

  1. 01Form testable hunting hypotheses
  2. 02Identify logs and telemetry sources
  3. 03Query SIEM and EDR data
  4. 04Correlate activity with attack TTPs
  5. 05Document findings and response actions

Use cases

  1. 01Hunt for Kerberoasting based on threat intelligence
  2. 02Investigate EDR or SIEM alerts
  3. 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-attacks

After installing, type/detecting-kerberoasting-attacksin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE
assets/
template.md
references/
api-reference.md
standards.md
workflows.md
scripts/
agent.py
process.py

Details

From the project README

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

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

Tools & Systems

ToolPurpose
CrowdStrike FalconEDR telemetry and threat detection
Microsoft Defender for EndpointAdvanced hunting with KQL
Splunk EnterpriseSIEM log analysis with SPL queries
Elastic SecurityDetection rules and investigation timeline
SysmonDetailed Windows event monitoring
VelociraptorEndpoint artifact collection and hunting
Sigma RulesCross-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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