threat hunting

Detecting Mimikatz Execution Patterns

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

credential-dumpingedrmimikatzmitre-attackproactive-detectiont1003threat-hunting
Install this skill
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Framework mappings

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]
Source materials

References and resources

Everything below is rendered for inspection. Script files are read-only and never run.

References 3

api-reference.md1.6 KB

API Reference: Detecting Mimikatz Execution Patterns

Mimikatz Command Signatures

Command MITRE Impact
sekurlsa::logonpasswords T1003.001 Dump all credentials
lsadump::dcsync T1003.006 DCSync attack
kerberos::golden T1558.001 Golden Ticket
kerberos::ptt T1550.003 Pass-the-Ticket
lsadump::sam T1003.002 SAM dump
misc::skeleton T1556.001 Skeleton Key

LSASS Dump Techniques

Method Detection Pattern
comsvcs.dll MiniDump rundll32.*comsvcs.*MiniDump
ProcDump procdump.*-ma.*lsass
SQLDumper sqldumper.*lsass
.NET createdump createdump.*lsass
PowerShell Out-Minidump.*lsass

Sysmon Detection Events

Event ID Usage
1 Process Create (mimikatz.exe)
7 Image Loaded (sekurlsa.dll)
10 Process Access (LSASS access mask)

Splunk SPL Detection

index=sysmon (EventCode=1 OR EventCode=10)
| where match(CommandLine, "(?i)(sekurlsa|lsadump|kerberos::golden|privilege::debug)")
   OR (TargetImage="*\\lsass.exe" AND GrantedAccess IN ("0x1010","0x1FFFFF"))
| table _time Image CommandLine GrantedAccess Computer

YARA Rule

rule Mimikatz_Strings {
    strings:
        $s1 = "sekurlsa::logonpasswords" ascii wide
        $s2 = "lsadump::dcsync" ascii wide
        $s3 = "kerberos::golden" ascii wide
        $s4 = "mimilib" ascii wide
    condition:
        any of them
}

CLI Usage

python agent.py --evtx-file Sysmon.evtx
python agent.py --text-log process_audit.log
standards.md1.6 KB

Standards and References - Detecting Mimikatz Execution Patterns

MITRE ATT&CK Mappings

Technique Name Description
T1003.001 LSASS Memory See attack.mitre.org/techniques/T1003/001
T1003.006 DCSync See attack.mitre.org/techniques/T1003/006
T1558.003 Kerberoasting See attack.mitre.org/techniques/T1558/003
T1558.001 Golden Ticket See attack.mitre.org/techniques/T1558/001

Detection Data Sources

Source Event ID Purpose
Sysmon 1 Process creation with command line
Sysmon 3 Network connection initiated
Sysmon 7 Image loaded (DLL)
Sysmon 10 Process access (LSASS)
Sysmon 11 File creation
Sysmon 12/13 Registry create/set
Sysmon 22 DNS query
Sysmon 25 Process tampering
Windows Security 4624 Successful logon
Windows Security 4625 Failed logon
Windows Security 4648 Explicit credential logon
Windows Security 4672 Special privileges assigned
Windows Security 4688 Process creation
Windows Security 4697 Service installed
Windows Security 4698 Scheduled task created
Windows Security 4769 Kerberos TGS requested
Windows Security 5140 Network share accessed

References

workflows.md2.8 KB

Detailed Hunting Workflow - Detecting Mimikatz Execution Patterns

Phase 1: Data Collection and Querying

Splunk SPL Query

index=sysmon EventCode=1
| where match(CommandLine, "(?i)(sekurlsa|lsadump|kerberos::list|privilege::debug|token::elevate|dpapi::)")
| table _time Computer User Image CommandLine ParentImage

KQL Query (Microsoft Defender for Endpoint)

DeviceProcessEvents
| where ProcessCommandLine has_any ("sekurlsa","lsadump","kerberos::","privilege::debug")
| project Timestamp, DeviceName, AccountName, FileName, ProcessCommandLine

Phase 2: Baseline and Anomaly Detection

Step 2.1 - Establish Normal Behavior Baseline

  • Collect 30 days of historical data for the targeted technique
  • Document expected patterns, frequencies, and legitimate use cases
  • Identify known false positive sources and document exceptions
  • Build statistical baseline (mean, standard deviation) for key metrics

Step 2.2 - Identify Anomalies

  • Compare current activity against the 30-day baseline
  • Flag events exceeding 3 standard deviations from normal
  • Prioritize anomalies by risk score and potential business impact
  • Cross-reference with threat intelligence for known IOCs

Phase 3: Investigation and Correlation

Step 3.1 - Deep Dive Analysis

  • For each anomaly, collect full process tree context
  • Correlate with network activity, file operations, and authentication events
  • Check binary signatures, file hashes, and certificate validity
  • Review user account context and access patterns

Step 3.2 - Attack Chain Reconstruction

  • Map findings to MITRE ATT&CK kill chain stages
  • Identify initial access vector if applicable
  • Trace lateral movement and privilege escalation paths
  • Determine data access and potential exfiltration

Phase 4: Validation and Response

Step 4.1 - True/False Positive Determination

  • Verify findings with system owners and IT operations
  • Check change management records for authorized activities
  • Validate user context (authorized actions vs. compromised account)
  • Document determination rationale for each finding

Step 4.2 - Response Actions

  • For confirmed threats: initiate incident response procedures
  • For detection gaps: create or update detection rules
  • For false positives: tune existing rules and update exclusions
  • Update threat hunting playbook with lessons learned

Phase 5: Documentation and Reporting

Step 5.1 - Hunt Report

  • Summarize hypothesis, methodology, and findings
  • Include all queries executed and their results
  • Document IOCs discovered and detection rules created
  • Provide recommendations for security improvements

Step 5.2 - Knowledge Base Update

  • Add findings to threat intelligence platform
  • Update MITRE ATT&CK coverage heatmap
  • Share detection rules via Sigma format
  • Schedule follow-up hunts for related techniques

Scripts 2

agent.py5.8 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Mimikatz execution pattern detection agent.

Detects Mimikatz and related credential theft tools by analyzing process
creation logs, LSASS access patterns, and known command-line signatures.
"""

import argparse
import json
import re
import sys
from datetime import datetime

try:
    import Evtx.Evtx as evtx
except ImportError:
    evtx = None

MIMIKATZ_CMDLINE_PATTERNS = [
    (r"sekurlsa::logonpasswords", "CRITICAL", "Credential dump via sekurlsa"),
    (r"sekurlsa::wdigest", "CRITICAL", "WDigest credential extraction"),
    (r"sekurlsa::kerberos", "CRITICAL", "Kerberos ticket extraction"),
    (r"lsadump::dcsync", "CRITICAL", "DCSync attack"),
    (r"lsadump::sam", "CRITICAL", "SAM database dump"),
    (r"lsadump::lsa\s*/patch", "CRITICAL", "LSA secrets dump"),
    (r"kerberos::golden", "CRITICAL", "Golden Ticket creation"),
    (r"kerberos::ptt", "HIGH", "Pass-the-Ticket"),
    (r"privilege::debug", "HIGH", "Debug privilege escalation"),
    (r"token::elevate", "HIGH", "Token elevation"),
    (r"crypto::capi", "MEDIUM", "Certificate export"),
    (r"dpapi::chrome", "HIGH", "Chrome credential extraction"),
    (r"vault::cred", "HIGH", "Credential Vault access"),
    (r"misc::skeleton", "CRITICAL", "Skeleton Key injection"),
]

MIMIKATZ_BINARY_INDICATORS = [
    (r"mimikatz\.exe", "CRITICAL"),
    (r"mimi(32|64)\.exe", "CRITICAL"),
    (r"mimikittenz", "CRITICAL"),
    (r"sekurlsa\.dll", "CRITICAL"),
    (r"mimilib\.dll", "CRITICAL"),
    (r"mimidrv\.sys", "CRITICAL"),
    (r"kiwi_passwords", "CRITICAL"),
]

LSASS_DUMP_PATTERNS = [
    (r"rundll32.*comsvcs.*MiniDump", "CRITICAL", "LSASS minidump via comsvcs.dll"),
    (r"procdump.*-ma.*lsass", "HIGH", "LSASS dump via ProcDump"),
    (r"sqldumper.*lsass", "HIGH", "LSASS dump via SQLDumper"),
    (r"createdump.*lsass", "HIGH", "LSASS dump via .NET createdump"),
    (r"taskmgr.*lsass.*dump", "MEDIUM", "LSASS dump via Task Manager"),
    (r"Out-Minidump.*lsass", "CRITICAL", "PowerShell LSASS minidump"),
]


def scan_evtx(filepath):
    if evtx is None:
        return {"error": "python-evtx not installed: pip install python-evtx"}
    findings = []
    with evtx.Evtx(filepath) as log:
        for record in log.records():
            xml = record.xml()
            event_id_match = re.search(r'<EventID[^>]*>(\d+)</EventID>', xml)
            if not event_id_match:
                continue
            event_id = int(event_id_match.group(1))
            if event_id not in (1, 4688, 10):
                continue

            cmdline = re.search(r'<Data Name="CommandLine">([^<]+)', xml)
            image = re.search(r'<Data Name="Image">([^<]+)', xml)
            new_proc = re.search(r'<Data Name="NewProcessName">([^<]+)', xml)
            time_match = re.search(r'SystemTime="([^"]+)"', xml)
            user = re.search(r'<Data Name="User">([^<]+)', xml)

            cmd = cmdline.group(1) if cmdline else ""
            proc = image.group(1) if image else (new_proc.group(1) if new_proc else "")

            for pattern, severity in MIMIKATZ_BINARY_INDICATORS:
                if re.search(pattern, proc, re.IGNORECASE):
                    findings.append({
                        "event_id": event_id,
                        "timestamp": time_match.group(1) if time_match else "",
                        "type": "mimikatz_binary",
                        "process": proc,
                        "severity": severity,
                        "mitre": "T1003.001",
                    })

            for pattern, severity, desc in MIMIKATZ_CMDLINE_PATTERNS:
                if re.search(pattern, cmd, re.IGNORECASE):
                    findings.append({
                        "event_id": event_id,
                        "timestamp": time_match.group(1) if time_match else "",
                        "type": "mimikatz_command",
                        "command": cmd[:300],
                        "description": desc,
                        "severity": severity,
                        "mitre": "T1003",
                    })

            for pattern, severity, desc in LSASS_DUMP_PATTERNS:
                if re.search(pattern, cmd, re.IGNORECASE):
                    findings.append({
                        "event_id": event_id,
                        "timestamp": time_match.group(1) if time_match else "",
                        "type": "lsass_dump",
                        "command": cmd[:300],
                        "description": desc,
                        "severity": severity,
                        "mitre": "T1003.001",
                    })

    return findings


def scan_text_log(filepath):
    findings = []
    with open(filepath, "r", encoding="utf-8", errors="replace") as f:
        for num, line in enumerate(f, 1):
            for pattern, severity, desc in MIMIKATZ_CMDLINE_PATTERNS + LSASS_DUMP_PATTERNS:
                if re.search(pattern, line, re.IGNORECASE):
                    findings.append({
                        "line": num, "severity": severity,
                        "description": desc, "excerpt": line.strip()[:200],
                    })
    return findings


def main():
    parser = argparse.ArgumentParser(description="Mimikatz Execution Pattern Detector")
    parser.add_argument("--evtx-file", help="Sysmon or Security EVTX file")
    parser.add_argument("--text-log", help="Text log file to scan")
    args = parser.parse_args()

    results = {"timestamp": datetime.utcnow().isoformat() + "Z", "findings": []}

    if args.evtx_file:
        evtx_findings = scan_evtx(args.evtx_file)
        if isinstance(evtx_findings, dict):
            results.update(evtx_findings)
        else:
            results["findings"].extend(evtx_findings)

    if args.text_log:
        results["findings"].extend(scan_text_log(args.text_log))

    results["total_findings"] = len(results["findings"])
    print(json.dumps(results, indent=2))


if __name__ == "__main__":
    main()
process.py3.6 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Mimikatz Detection - Analyzes logs for T1003.001 indicators."""

import json, csv, argparse, datetime, re
from collections import defaultdict
from pathlib import Path

DETECTION_PATTERNS = [
    r'sekurlsa::',
    r'lsadump::',
    r'kerberos::list',
    r'privilege::debug',
    r'token::elevate',
    r'dpapi::',
    r'vault::cred',
    r'crypto::cng',
    r'Invoke-Mimikatz',
    r'mimikatz',
    r'gentilkiwi',
]

def parse_logs(path):
    p = Path(path)
    if p.suffix == ".json":
        with open(p, encoding="utf-8") as f:
            data = json.load(f)
            return data if isinstance(data, list) else data.get("events", [])
    elif p.suffix == ".csv":
        with open(p, encoding="utf-8-sig") as f:
            return [dict(r) for r in csv.DictReader(f)]
    return []

def analyze_event(event):
    cmd = event.get("CommandLine", event.get("command_line", event.get("ProcessCommandLine", "")))
    content = event.get("Task_Content", event.get("Parameters", event.get("RawEventData", "")))
    search_text = f"{cmd} {content}"
    risk = 0
    indicators = []
    for pattern in DETECTION_PATTERNS:
        if re.search(pattern, search_text, re.IGNORECASE):
            risk += 25
            indicators.append(f"Pattern match: {pattern}")
    if not indicators:
        return None
    risk = min(risk, 100)
    return {
        "technique": "T1003.001",
        "command_line": cmd[:500] if cmd else content[:500],
        "hostname": event.get("Computer", event.get("DeviceName", event.get("hostname", "unknown"))),
        "user": event.get("User", event.get("AccountName", event.get("UserId", "unknown"))),
        "timestamp": event.get("_time", event.get("timestamp", event.get("UtcTime", event.get("Timestamp", "")))),
        "risk_score": risk,
        "risk_level": "CRITICAL" if risk >= 75 else "HIGH" if risk >= 50 else "MEDIUM" if risk >= 25 else "LOW",
        "indicators": indicators,
    }

def run_hunt(input_path, output_dir):
    print(f"[*] Mimikatz Hunt - {datetime.datetime.now().isoformat()}")
    events = parse_logs(input_path)
    findings = [f for f in (analyze_event(e) for e in events) if f]
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    slug = "detecting_mimikatz_e"
    with open(Path(output_dir) / f"{slug}_findings.json", "w", encoding="utf-8") as f:
        json.dump({"hunt_id": f"TH-{datetime.date.today()}", "total_events": len(events), "findings": findings}, f, indent=2)
    with open(Path(output_dir) / "hunt_report.md", "w", encoding="utf-8") as f:
        f.write(f"# Mimikatz Hunt Report\n\n")
        f.write(f"**Date**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
        f.write(f"**Findings**: {len(findings)}\n\n")
        for finding in sorted(findings, key=lambda x: x["risk_score"], reverse=True)[:20]:
            f.write(f"### [{finding['risk_level']}] {finding['technique']}\n")
            f.write(f"- **Host**: {finding['hostname']}\n")
            f.write(f"- **Indicators**: {', '.join(finding['indicators'])}\n\n")
    print(f"[+] {len(findings)} findings written to {output_dir}")

def main():
    p = argparse.ArgumentParser(description="Mimikatz Detection")
    sp = p.add_subparsers(dest="cmd")
    h = sp.add_parser("hunt"); h.add_argument("--input", "-i", required=True); h.add_argument("--output", "-o", default="./detecting_mimik_output")
    sp.add_parser("queries")
    args = p.parse_args()
    if args.cmd == "hunt": run_hunt(args.input, args.output)
    elif args.cmd == "queries":
        print("=== Detection Queries ===")
        print("See references/workflows.md for platform-specific queries")
    else: p.print_help()

if __name__ == "__main__": main()

Assets 1

template.mdtext/markdown · 2.6 KB
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