detecting-service-account-abuse skill (Anthropic-Cybersecurity-Skills)

From Public Agent Wiki

What it does. Detect abuse of service accounts by hunting for anomalous interactive Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/detecting-service-account-abuse/SKILL.md
License Apache-2.0 (skill folder LICENSE)
Author mukul975
Fetched 2026-09-10

Install

  • npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-service-account-abuse, or copy the skill folder into ~/.claude/skills/detecting-service-account-abuse/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/detecting-service-account-abuse/SKILL.md

SKILL.md (verbatim)

name: detecting-service-account-abuse
description: 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: cybersecurity
subdomain: threat-hunting
tags:
- threat-hunting
- mitre-attack
- service-accounts
- privilege-escalation
- t1078
- proactive-detection
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Restore Access
- Password Authentication
- Biometric Authentication
- Strong Password Policy
- Restore User Account Access
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_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

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

  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]

Other files in this skill

assets/template.md (verbatim)

Detecting Service Account Abuse - Hunt Template

Hunt Metadata

Field Value
Hunt ID TH-DETECT-YYYY-MM-DD-NNN
Analyst
Date Started
Date Completed
Status [ ] In Progress / [ ] Complete
Priority [ ] Critical / [ ] High / [ ] Medium / [ ] Low

Hypothesis

Statement: [Formulate a clear, testable hypothesis]

Basis: [ ] Threat Intel / [ ] ATT&CK Gap / [ ] Anomaly / [ ] Incident Follow-up

Target Techniques

  • T1078.002 - Domain Accounts
  • T1078.001 - Default Accounts
  • T1021 - Remote Services

Data Sources

  • Sysmon Event Logs
  • Windows Security Event Logs
  • EDR Telemetry (Platform: _____________)
  • SIEM (Platform: _____________)
  • Network Logs (Proxy/Firewall/DNS)
  • Cloud Audit Logs
  • Email Gateway Logs
  • Application Logs

Queries Executed

Query 1: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Query 2: [Description]

[Query text]

Results: [Count] events | Execution Time: [Duration]

Findings

# Timestamp Host User Technique Evidence Summary Risk Verdict
1 TP / FP / BTP
2 TP / FP / BTP
3 TP / FP / BTP

IOCs Discovered

Network IOCs

Type Value Context Confidence
IP
Domain
URL

Host IOCs

Type Value Context Confidence
SHA256
Filename
Registry Key
Scheduled Task

Hunt Results Summary

Metric Count
Total Events Analyzed
Anomalies Identified
True Positives
False Positives
Benign True Positives
New IOCs Discovered
Detection Rules Created
Detection Rules Updated

Hypothesis Outcome

  • Confirmed: Evidence supports the hypothesis
  • Partially Confirmed: Some evidence found, further investigation needed
  • Refuted: No evidence found
  • Inconclusive: Insufficient data

Recommendations

  1. Immediate Actions: [Containment, remediation steps]
  2. Detection Improvements: [New rules, tuning recommendations]
  3. Visibility Gaps: [Missing data sources, coverage needs]
  4. Security Hardening: [Configuration changes, policy updates]
  5. Follow-up Hunts: [Related hypotheses to investigate]

Analyst Notes

[Free-form notes, observations, and lessons learned]

references/api-reference.md (verbatim)

API Reference: Service Account Abuse Detection

Active Directory PowerShell Module

Get Service Accounts (SPN-based)

Get-ADUser -Filter {ServicePrincipalName -ne "$null"} -Properties `
    ServicePrincipalName, LastLogonDate, Enabled, PasswordLastSet, `
    PasswordNeverExpires, AdminCount, MemberOf

Get Managed Service Accounts

Get-ADServiceAccount -Filter * -Properties `
    PrincipalsAllowedToRetrieveManagedPassword, LastLogonDate

Check Kerberos Delegation

Get-ADUser -Filter {TrustedForDelegation -eq $true} -Properties `
    TrustedForDelegation, TrustedToAuthForDelegation, `
    msDS-AllowedToDelegateTo

Windows Event Log Queries

Logon Type Values

Type Description Concern for Service Accounts
2 Interactive HIGH — should not happen
3 Network Normal for services
5 Service Normal
10 RemoteInteractive (RDP) HIGH — should not happen

Event IDs

ID Log Description
4624 Security Successful logon
4625 Security Failed logon
4648 Security Explicit credential use
4672 Security Special privilege logon
4720 Security Account created
4738 Security Account modified

Microsoft Graph API — Service Principal Audit

List Service Principals

GET https://graph.microsoft.com/v1.0/servicePrincipals
Authorization: Bearer {token}

List App Role Assignments

GET https://graph.microsoft.com/v1.0/servicePrincipals/{id}/appRoleAssignments

Audit Sign-In Logs

GET https://graph.microsoft.com/v1.0/auditLogs/signIns
    ?$filter=appId eq '{service-principal-app-id}'

AWS IAM — Service Role Audit

List service-linked roles

aws iam list-roles --query "Roles[?starts_with(Path, '/aws-service-role/')]"

Get role last used

aws iam get-role --role-name MyServiceRole \
    --query "Role.RoleLastUsed"

Access Analyzer findings

aws accessanalyzer list-findings --analyzer-arn {arn} \
    --filter '{"resourceType":{"eq":["AWS::IAM::Role"]}}'

references/standards.md (verbatim)

Standards and References - Detecting Service Account Abuse

MITRE ATT&CK Mappings

Technique Name Description
T1078.002 Domain Accounts See attack.mitre.org/techniques/T1078/002
T1078.001 Default Accounts See attack.mitre.org/techniques/T1078/001
T1021 Remote Services See attack.mitre.org/techniques/T1021

Detection Data Sources

Source Event ID Purpose
Sysmon 1 Process creation with command line
Sysmon 3 Network connection initiated
Sysmon 7 Image loaded (DLL)
Sysmon 10 Process access (LSASS)
Sysmon 11 File creation
Sysmon 12/13 Registry create/set
Sysmon 22 DNS query
Sysmon 25 Process tampering
Windows Security 4624 Successful logon
Windows Security 4625 Failed logon
Windows Security 4648 Explicit credential logon
Windows Security 4672 Special privileges assigned
Windows Security 4688 Process creation
Windows Security 4697 Service installed
Windows Security 4698 Scheduled task created
Windows Security 4769 Kerberos TGS requested
Windows Security 5140 Network share accessed

References

references/workflows.md (verbatim)

Detailed Hunting Workflow - Detecting Service Account Abuse

Phase 1: Data Collection and Querying

Splunk SPL Query

index=wineventlog EventCode=4624 Logon_Type IN (2, 10)
| where match(Account_Name, "(?i)(svc_|service|admin_)")
| stats count values(Computer) as hosts by Account_Name Source_Network_Address
| sort -count

KQL Query (Microsoft Defender for Endpoint)

SecurityEvent
| where EventID == 4624 and LogonType in (2, 10)
| where SubjectUserName startswith "svc_" or SubjectUserName contains "service"
| summarize count(), Hosts=make_set(Computer) by SubjectUserName, IpAddress

Phase 2: Baseline and Anomaly Detection

Step 2.1 - Establish Normal Behavior Baseline

  • Collect 30 days of historical data for the targeted technique
  • Document expected patterns, frequencies, and legitimate use cases
  • Identify known false positive sources and document exceptions
  • Build statistical baseline (mean, standard deviation) for key metrics

Step 2.2 - Identify Anomalies

  • Compare current activity against the 30-day baseline
  • Flag events exceeding 3 standard deviations from normal
  • Prioritize anomalies by risk score and potential business impact
  • Cross-reference with threat intelligence for known IOCs

Phase 3: Investigation and Correlation

Step 3.1 - Deep Dive Analysis

  • For each anomaly, collect full process tree context
  • Correlate with network activity, file operations, and authentication events
  • Check binary signatures, file hashes, and certificate validity
  • Review user account context and access patterns

Step 3.2 - Attack Chain Reconstruction

  • Map findings to MITRE ATT&CK kill chain stages
  • Identify initial access vector if applicable
  • Trace lateral movement and privilege escalation paths
  • Determine data access and potential exfiltration

Phase 4: Validation and Response

Step 4.1 - True/False Positive Determination

  • Verify findings with system owners and IT operations
  • Check change management records for authorized activities
  • Validate user context (authorized actions vs. compromised account)
  • Document determination rationale for each finding

Step 4.2 - Response Actions

  • For confirmed threats: initiate incident response procedures
  • For detection gaps: create or update detection rules
  • For false positives: tune existing rules and update exclusions
  • Update threat hunting playbook with lessons learned

Phase 5: Documentation and Reporting

Step 5.1 - Hunt Report

  • Summarize hypothesis, methodology, and findings
  • Include all queries executed and their results
  • Document IOCs discovered and detection rules created
  • Provide recommendations for security improvements

Step 5.2 - Knowledge Base Update

  • Add findings to threat intelligence platform
  • Update MITRE ATT&CK coverage heatmap
  • Share detection rules via Sigma format
  • Schedule follow-up hunts for related techniques

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