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detecting-service-account-abuse

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.

Published by mukul975 ↗mukul975/Anthropic-Cybersecurity-Skills

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detecting-service-account-abuse

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Use detecting-service-account-abuse to investigate [service account or alert] using available [SIEM/EDR telemetry], producing detection queries, validated findings, correlated activity, and recommended response actions.

Required inputs

  • [service account or alert]
  • [SIEM/EDR telemetry]

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SKILL.md

name: detecting-service-account-abusedescription: 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.domain: cybersecuritysubdomain: threat-huntingtags: - threat-hunting- mitre-attack- service-accounts- privilege-escalation- t1078- proactive-detectionversion: '1.0'author: mahipallicense: Apache-2.0d3fend_techniques: - Restore Access- Password Authentication- Biometric Authentication- Strong Password Policy- Restore User Account Accessnist_csf: - DE.CM-01- DE.AE-02- DE.AE-07- ID.RA-05mitre_attack: - T1078.002- T1021.001- T1098.001- T1550.002

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

  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
T1078.002Domain Accounts
T1078.001Default Accounts
T1021Remote Services

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: Service account RDP to domain controller
  2. Scenario 2: SQL service accessing file shares outside scope
  3. Scenario 3: Backup service lateral movement off-hours
  4. 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]