DE
detecting-mimikatz-execution-patterns
Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory
Install
mkdir -p .claude/skills/detecting-mimikatz-execution-patterns && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16320" && unzip -o skill.zip -d .claude/skills/detecting-mimikatz-execution-patterns && rm skill.zipInstalls to .claude/skills/detecting-mimikatz-execution-patterns
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory114 charsno explicit “when” trigger
About this skill
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
- Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
- Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
- Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
- Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
- Validate Findings: Distinguish true positives from false positives through contextual analysis.
- Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
- 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
- Scenario 1: Standard sekurlsa::logonpasswords credential dump
- Scenario 2: PowerShell Invoke-Mimikatz reflective loading
- Scenario 3: DCSync from non-DC host
- 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]