detecting-mimikatz-execution-patterns skill (Anthropic-Cybersecurity-Skills)

From Public Agent Wiki

What it does. Detect Mimikatz credential-dumping activity via command-line pattern matching, LSASS access signatures, binary/hash indicators, and in-memory detection of known Mimikatz modules. Use when threat hunting for T1003 credential access, triaging EDR/SIEM alerts on LSASS access, scoping compromise during incident response, or validating detection coverage in a purple team exercise. Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/detecting-mimikatz-execution-patterns/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-mimikatz-execution-patterns, or copy the skill folder into ~/.claude/skills/detecting-mimikatz-execution-patterns/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/detecting-mimikatz-execution-patterns/SKILL.md

SKILL.md (verbatim)

name: detecting-mimikatz-execution-patterns
description: Detect Mimikatz credential-dumping activity via command-line pattern matching, LSASS access signatures, binary/hash indicators, and in-memory detection of known Mimikatz modules. Use when threat hunting for T1003 credential access, triaging EDR/SIEM alerts on LSASS access, scoping compromise during incident response, or validating detection coverage in a purple team exercise.
domain: cybersecurity
subdomain: threat-hunting
tags:
- threat-hunting
- mitre-attack
- mimikatz
- credential-dumping
- edr
- t1003
- proactive-detection
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Execution Isolation
- Process Termination
- Hardware-based Process Isolation
- Web Session Access Mediation
- Process Suspension
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1046
- T1057
- T1082
- T1083
- T1003

Detecting Mimikatz Execution Patterns

When to Use

  • When proactively hunting for indicators of detecting mimikatz execution patterns 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
T1003.001 LSASS Memory
T1003.006 DCSync
T1558.003 Kerberoasting
T1558.001 Golden Ticket

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: Standard sekurlsa::logonpasswords credential dump
  2. Scenario 2: PowerShell Invoke-Mimikatz reflective loading
  3. Scenario 3: DCSync from non-DC host
  4. Scenario 4: Golden ticket creation for persistence

Output Format

Hunt ID: TH-DETECT-[DATE]-[SEQ]
Technique: T1003.001
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 Mimikatz Execution Patterns - 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

  • T1003.001 - LSASS Memory
  • T1003.006 - DCSync
  • T1558.003 - Kerberoasting
  • T1558.001 - Golden Ticket

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: Detecting Mimikatz Execution Patterns

Mimikatz Command Signatures

Command MITRE Impact
sekurlsa::logonpasswords T1003.001 Dump all credentials
lsadump::dcsync T1003.006 DCSync attack
kerberos::golden T1558.001 Golden Ticket
kerberos::ptt T1550.003 Pass-the-Ticket
lsadump::sam T1003.002 SAM dump
misc::skeleton T1556.001 Skeleton Key

LSASS Dump Techniques

Method Detection Pattern
comsvcs.dll MiniDump rundll32.*comsvcs.*MiniDump
ProcDump procdump.*-ma.*lsass
SQLDumper sqldumper.*lsass
.NET createdump createdump.*lsass
PowerShell Out-Minidump.*lsass

Sysmon Detection Events

Event ID Usage
1 Process Create (mimikatz.exe)
7 Image Loaded (sekurlsa.dll)
10 Process Access (LSASS access mask)

Splunk SPL Detection

index=sysmon (EventCode=1 OR EventCode=10)
| where match(CommandLine, "(?i)(sekurlsa|lsadump|kerberos::golden|privilege::debug)")
   OR (TargetImage="*\\lsass.exe" AND GrantedAccess IN ("0x1010","0x1FFFFF"))
| table _time Image CommandLine GrantedAccess Computer

YARA Rule

rule Mimikatz_Strings {
    strings:
        $s1 = "sekurlsa::logonpasswords" ascii wide
        $s2 = "lsadump::dcsync" ascii wide
        $s3 = "kerberos::golden" ascii wide
        $s4 = "mimilib" ascii wide
    condition:
        any of them
}

CLI Usage

python agent.py --evtx-file Sysmon.evtx
python agent.py --text-log process_audit.log

references/standards.md (verbatim)

Standards and References - Detecting Mimikatz Execution Patterns

MITRE ATT&CK Mappings

Technique Name Description
T1003.001 LSASS Memory See attack.mitre.org/techniques/T1003/001
T1003.006 DCSync See attack.mitre.org/techniques/T1003/006
T1558.003 Kerberoasting See attack.mitre.org/techniques/T1558/003
T1558.001 Golden Ticket See attack.mitre.org/techniques/T1558/001

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 Mimikatz Execution Patterns

Phase 1: Data Collection and Querying

Splunk SPL Query

index=sysmon EventCode=1
| where match(CommandLine, "(?i)(sekurlsa|lsadump|kerberos::list|privilege::debug|token::elevate|dpapi::)")
| table _time Computer User Image CommandLine ParentImage

KQL Query (Microsoft Defender for Endpoint)

DeviceProcessEvents
| where ProcessCommandLine has_any ("sekurlsa","lsadump","kerberos::","privilege::debug")
| project Timestamp, DeviceName, AccountName, FileName, ProcessCommandLine

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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