What it does. Runs a hypothesis-driven threat hunt for Windows Scheduled Task persistence (T1053), guiding SIEM/EDR queries against task creation events (e.g. Event ID 4698), suspicious task actions, and unusual scheduling patterns. Use when hunting for scheduled-task persistence, after threat intel flags related campaigns, during incident response, or when alerts fire on schtasks/at.exe activity. Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Upstream	mukul975/Anthropic-Cybersecurity-Skills
Skill file	skills/hunting-for-scheduled-task-persistence/SKILL.md
License	Apache-2.0 (skill folder LICENSE)
Author	mukul975
Fetched	2026-09-10

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill hunting-for-scheduled-task-persistence, or copy the skill folder into ~/.claude/skills/hunting-for-scheduled-task-persistence/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/hunting-for-scheduled-task-persistence/SKILL.md

SKILL.md (verbatim)

name: hunting-for-scheduled-task-persistence
description: Runs a hypothesis-driven threat hunt for Windows Scheduled Task persistence (T1053), guiding SIEM/EDR queries against task creation events (e.g. Event ID 4698), suspicious task actions, and unusual scheduling patterns. Use when hunting for scheduled-task persistence, after threat intel flags related campaigns, during incident response, or when alerts fire on schtasks/at.exe activity.
domain: cybersecurity
subdomain: threat-hunting
tags:
- threat-hunting
- mitre-attack
- scheduled-tasks
- persistence
- t1053
- proactive-detection
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Execution Isolation
- Process Termination
- Hardware-based Process Isolation
- Platform Monitoring
- Process Suspension
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1046
- T1057
- T1082
- T1083
- T1547

Hunting For Scheduled Task Persistence

When to Use

• When proactively hunting for indicators of hunting for scheduled task persistence 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
T1053.005	Scheduled Task
T1053.003	Cron
T1053.002	At

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: Cobalt Strike persistence via schtasks creating periodic beacon
2. Scenario 2: Ransomware scheduled task for re-execution after reboot
3. Scenario 3: APT encoded PowerShell task running every 30 minutes
4. Scenario 4: Insider task to periodically copy sensitive files

Output Format

Hunt ID: TH-HUNTIN-[DATE]-[SEQ]
Technique: T1053.005
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

• LICENSE
• assets/template.md
• references/api-reference.md
• references/standards.md
• references/workflows.md
• scripts/agent.py
• scripts/process.py

assets/template.md (verbatim)

Hunting For Scheduled Task Persistence - Hunt Template

Hunt Metadata

Field	Value
Hunt ID	TH-HUNTIN-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

• T1053.005 - Scheduled Task
• T1053.003 - Cron
• T1053.002 - At

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 — Hunting for Scheduled Task Persistence

Libraries Used

• subprocess: Execute schtasks /query and schtasks /query /xml for task enumeration
• csv: Parse schtasks CSV output for structured task analysis
• python-evtx (Evtx): Parse Security EVTX for Event ID 4698 (Task Created)

CLI Interface

python agent.py enumerate                    # List and risk-score all tasks
python agent.py events --evtx-file <path>    # Scan EVTX for task creation events
python agent.py export --task-name <name>    # Export task XML definition

Core Functions

enumerate_tasks()

Runs schtasks /query /fo CSV /v and classifies each task as high/medium/low risk.

Returns: dict with total_tasks, high_risk, medium_risk, suspicious_tasks, non_vendor_tasks.

scan_event_log_4698(evtx_file)

Parses Windows Security EVTX for Event ID 4698 (Scheduled Task Created).

Parameters:

Name	Type	Description
evtx_file	str	Path to Security .evtx log file

export_task_xml(task_name)

Exports a task's full XML definition using schtasks /query /tn <name> /xml.

Risk Classification

Risk	Criteria
High	Action matches suspicious patterns (powershell -enc, certutil, temp paths)
Medium	Non-vendor task (not under \Microsoft\, \Google\, etc.)
Low	Known vendor task prefix

Dependencies

pip install python-evtx  # Optional, for EVTX parsing

references/standards.md (verbatim)

Standards and References - Hunting For Scheduled Task Persistence

MITRE ATT&CK Mappings

Technique	Name	Description
T1053.005	Scheduled Task	See attack.mitre.org/techniques/T1053/005
T1053.003	Cron	See attack.mitre.org/techniques/T1053/003
T1053.002	At	See attack.mitre.org/techniques/T1053/002

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

• MITRE ATT&CK Framework: https://attack.mitre.org/
• Sigma Detection Rules: https://github.com/SigmaHQ/sigma
• LOLBAS Project: https://lolbas-project.github.io/
• Atomic Red Team Tests: https://github.com/redcanaryco/atomic-red-team
• Red Canary Threat Detection Report
• SANS Threat Hunting Summit Resources

references/workflows.md (verbatim)

Detailed Hunting Workflow - Hunting For Scheduled Task Persistence

Phase 1: Data Collection and Querying

Splunk SPL Query

index=wineventlog (EventCode=4698 OR EventCode=106)
| where match(Task_Content, "(?i)(powershell|cmd|wscript|mshta|http|encoded)")
| table _time Computer User Task_Name Task_Content

KQL Query (Microsoft Defender for Endpoint)

DeviceEvents
| where ActionType == "ScheduledTaskCreated"
| where AdditionalFields has_any ("powershell","cmd","wscript","http")
| project Timestamp, DeviceName, AccountName, AdditionalFields

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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