AGENT SKILL

detecting-mimikatz-execution-patterns

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

License 许可
Apache-2.0
最近更新
2026-11-14
Stars
★ 13
Forks
2

01 前言 Front Matter

name
detecting-mimikatz-execution-patterns
tags
[ "threat-hunting", "mitre-attack", "mimikatz", "credential-dumping", "edr", "t1003", "proactive-detection" ]
author
mahipal
domain
cybersecurity
source
https://github.com/mukul975/Anthropic-Cybersecurity-Skills
license
Apache-2.0
version
1.0
nist_csf
[ "DE.CM-01", "DE.AE-02", "DE.AE-07", "ID.RA-05" ]
subdomain
threat-hunting
description
Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.
mitre_attack
[ "T1046", "T1057", "T1082", "T1083", "T1003" ]
source_commit
04450304b12645cb2b974ab96d28c0664758a88d
d3fend_techniques
[ "Execution Isolation", "Process Termination", "Hardware-based Process Isolation", "Web Session Access Mediation", "Process Suspension" ]

02 资源文件 7

03 SKILL.md 正文

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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