# Detecting Mimikatz Execution Patterns

> Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

- Skill: `thedixitjain/detecting-mimikatz-execution-patterns` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds add thedixitjain/detecting-mimikatz-execution-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/detecting-mimikatz-execution-patterns/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/thedixitjain/detecting-mimikatz-execution-patterns

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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]
```

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**Source:** [`mukul975/Anthropic-Cybersecurity-Skills`](https://github.com/mukul975/Anthropic-Cybersecurity-Skills) → `skills/detecting-mimikatz-execution-patterns/SKILL.md`