mukul975/detecting-service-account-abuse
skills/detecting-service-account-abuse/SKILL.md
Detect abuse of service accounts by hunting for anomalous interactive logons, privilege escalation, and lateral movement using EDR/SIEM telemetry (CrowdStrike Falcon, Microsoft Defender, Splunk, Elastic Security, Sysmon, Velociraptor) and Sigma detection rules. Use when hunting for service-account misuse or investigating a service account performing unexpected interactive logons.
$ install --global
skillsauthnpx skillsauth add mukul975/anthropic-cybersecurity-skills detecting-service-account-abuseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Aug 9, 2026, 3:34 AM128.7s7 files scanned
SKILL.md
- name:
- detecting-service-account-abuse
- description:
- Detect abuse of service accounts by hunting for anomalous interactive
- domain:
- cybersecurity
- subdomain:
- threat-hunting
- version:
- 1.0
- author:
- mahipal
- license:
- Apache-2.0
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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