serviceaccount-service-detect
Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.
- License 许可
- MIT
- 最近更新
- 2026-09-01
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- cwe
- [ "CWE-269" ]
- name
- serviceaccount-service-detect
- tags
- [ "threat-hunting", "mitre-attack", "service-accounts", "privilege-escalation", "t1078", "proactive-detection" ]
- capec
- []
- model
- sonnet
- tools
- [ "Read", "Bash", "Glob", "Grep" ]
- domain
- cybersecurity
- maxTurns
- 20
- nist_csf
- [ "DE.CM-01", "DE.AE-02", "DE.AE-07", "ID.RA-05" ]
- subdomain
- threat-hunting
- description
- Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.
- mitre_attack
- [ "T1021", "T1059", "T1068", "T1070" ]
- d3fend_techniques
- [ "Restore Access", "Password Authentication", "Biometric Authentication", "Strong Password Policy", "Restore User Account Access" ]
02 资源文件 1
- 参考文档index.json648 B
03 SKILL.md 正文
Detecting Service Account Abuse
When to Use
- When proactively hunting for indicators of detecting service account abuse 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 |
|---|---|
| T1078.002 | Domain Accounts |
| T1078.001 | Default Accounts |
| T1021 | Remote Services |
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: Service account RDP to domain controller
- Scenario 2: SQL service accessing file shares outside scope
- Scenario 3: Backup service lateral movement off-hours
- Scenario 4: Compromised svc with DA privileges used for DCSync
Output Format
Hunt ID: TH-DETECT-[DATE]-[SEQ]
Technique: T1078.002
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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