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Anthropic-Cybersecurity-Skills/skills/detecting-mimikatz-execution-patterns/SKILL.md
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mukul975 ef27f026cb feat: enrich 209 skills with MITRE ATLAS, D3FEND, and NIST AI RMF frontmatter
Added structured security framework mappings to SKILL.md frontmatter across all applicable skills:
- atlas_techniques: MITRE ATLAS v5.5 AML.TXXXX IDs (81 skills, AI-targeted attack techniques)
- d3fend_techniques: MITRE D3FEND v1.3 defensive technique labels (139 skills, mapped from ATT&CK IDs)
- nist_ai_rmf: NIST AI RMF 1.0 subcategory IDs (85 skills, AI risk management functions)

Also updates ATTACK_COVERAGE.md with coverage statistics for all three frameworks.
2026-04-06 01:56:17 +02:00

3.2 KiB

name, description, domain, subdomain, tags, version, author, license, d3fend_techniques
name description domain subdomain tags version author license d3fend_techniques
detecting-mimikatz-execution-patterns Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules. cybersecurity threat-hunting
threat-hunting
mitre-attack
mimikatz
credential-dumping
edr
t1003
proactive-detection
1.0 mahipal Apache-2.0
Execution Isolation
Process Termination
Hardware-based Process Isolation
Web Session Access Mediation
Process Suspension

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]