detecting-kerberoasting-attacks
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
- License 许可
- Apache-2.0
- 最近更新
- 2026-05-06
- Stars
- ★ 14
- Forks
- 1
01 前言 Front Matter
- name
- detecting-kerberoasting-attacks
- tags
- [ "threat-hunting", "mitre-attack", "kerberoasting", "credential-access", "kerberos", "t1558", "proactive-detection" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0
- subdomain
- threat-hunting
- description
- Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
02 资源文件 7
- 资产文件assets/template.md2.6 KB
- 许可证LICENSE11.0 KB
- 参考文档references/api-reference.md1.4 KB
- 参考文档references/standards.md1.5 KB
- 参考文档references/workflows.md2.9 KB
- 脚本scripts/agent.py7.6 KB
- 脚本scripts/process.py3.5 KB
03 SKILL.md 正文
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]
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