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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

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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]