detecting-kerberoasting-attacks
$
npx mdskill add mukul975/Anthropic-Cybersecurity-Skills/detecting-kerberoasting-attacksMonitor Kerberos TGS requests to catch Kerberoasting attempts.
- Identify credential theft via service account password cracking.
- Integrates with EDR, SIEM, Sysmon, and Windows Security Logs.
- Correlates threat intelligence feeds with detected attack indicators.
- Generates actionable alerts for incident response teams.
SKILL.md
.github/skills/detecting-kerberoasting-attacksView on GitHub ↗
--- name: detecting-kerberoasting-attacks description: Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking. 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 --- # 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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