threat hunting

Hunting For Shadow Copy Deletion

Hunt for Volume Shadow Copy deletion activity that indicates ransomware preparation or anti-forensics by monitoring vssadmin, wmic, and PowerShell shadow copy commands.

anti-forensicsmitre-attackproactive-detectionransomwareshadow-copyt1490threat-hunting
Install this skill
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Framework mappings

When to Use

  • When proactively hunting for indicators of hunting for shadow copy deletion 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
T1490 Inhibit System Recovery
T1486 Data Encrypted for Impact
T1485 Data Destruction

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: Ransomware deleting shadow copies before encryption
  2. Scenario 2: vssadmin delete shadows /all /quiet pre-encryption
  3. Scenario 3: WMIC shadowcopy delete via PowerShell
  4. Scenario 4: bcdedit disabling recovery mode before impact

Output Format

Hunt ID: TH-HUNTIN-[DATE]-[SEQ]
Technique: T1490
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: Hunting for Shadow Copy Deletion

python-evtx

import Evtx.Evtx as evtx
 
with evtx.Evtx("Security.evtx") as log:
    for record in log.records():
        xml_str = record.xml()       # full XML of event
        ts = record.timestamp()      # datetime object

Detection Patterns

Pattern Technique Severity
vssadmin delete shadows T1490 CRITICAL
wmic shadowcopy delete T1490 CRITICAL
bcdedit /set recoveryenabled no T1490 HIGH
wbadmin delete catalog T1490 HIGH
Win32_ShadowCopy.Delete() T1490 CRITICAL

Splunk SPL

index=wineventlog (EventCode=4688 OR EventCode=1)
| where match(CommandLine, "(?i)(vssadmin.*delete.*shadows|wmic.*shadowcopy.*delete)")
| table _time Computer User CommandLine ParentImage

KQL (Microsoft Sentinel)

DeviceProcessEvents
| where ProcessCommandLine has_any ("vssadmin delete shadows", "wmic shadowcopy delete",
    "bcdedit /set", "wbadmin delete catalog")
| project Timestamp, DeviceName, AccountName, ProcessCommandLine, InitiatingProcessFileName

Sigma Rule Format

title: Shadow Copy Deletion
logsource:
  category: process_creation
  product: windows
detection:
  selection:
    Image|endswith: '\vssadmin.exe'
    CommandLine|contains|all:
      - 'delete'
      - 'shadows'
  condition: selection
level: critical
tags:
  - attack.impact
  - attack.t1490

References

standards.md1.5 KB

Standards and References - Hunting For Shadow Copy Deletion

MITRE ATT&CK Mappings

Technique Name Description
T1490 Inhibit System Recovery See attack.mitre.org/techniques/T1490
T1486 Data Encrypted for Impact See attack.mitre.org/techniques/T1486
T1485 Data Destruction See attack.mitre.org/techniques/T1485

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 - Hunting For Shadow Copy Deletion

Phase 1: Data Collection and Querying

Splunk SPL Query

index=sysmon EventCode=1
| where match(CommandLine, "(?i)(vssadmin.*delete|wmic.*shadowcopy.*delete|bcdedit.*recoveryenabled.*no|wbadmin.*delete)")
| table _time Computer User Image CommandLine ParentImage

KQL Query (Microsoft Defender for Endpoint)

DeviceProcessEvents
| where ProcessCommandLine has_any ("vssadmin delete","shadowcopy delete","bcdedit","recoveryenabled no","wbadmin delete")
| 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.py6.7 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Agent for hunting shadow copy deletion activity indicating ransomware or anti-forensics."""

import json
import argparse
import re
import xml.etree.ElementTree as ET
from datetime import datetime
from pathlib import Path

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


SHADOW_PATTERNS = [
    r"vssadmin\s+delete\s+shadows",
    r"vssadmin\.exe.*delete.*shadows",
    r"wmic\s+shadowcopy\s+delete",
    r"Get-WmiObject\s+Win32_ShadowCopy.*Delete",
    r"gwmi\s+Win32_ShadowCopy.*Remove",
    r"bcdedit.*recoveryenabled.*no",
    r"bcdedit.*/set.*bootstatuspolicy\s+ignoreallfailures",
    r"wbadmin\s+delete\s+catalog",
    r"Win32_ShadowCopy.*\.Delete",
    r"powershell.*shadowcopy.*delete",
]

RECOVERY_DISABLE_PATTERNS = [
    r"bcdedit\s+/set\s+\{default\}\s+recoveryenabled\s+no",
    r"reagentc\s+/disable",
    r"vssadmin\s+resize\s+shadowstorage.*maxsize=",
]


def parse_evtx_file(evtx_path):
    """Parse Windows EVTX file for shadow copy deletion events."""
    if evtx is None:
        return []
    events = []
    with evtx.Evtx(evtx_path) as log:
        for record in log.records():
            xml_str = record.xml()
            try:
                root = ET.fromstring(xml_str)
                ns = {"ns": "http://schemas.microsoft.com/win/2004/08/events/event"}
                event_id_el = root.find(".//ns:EventID", ns)
                if event_id_el is None:
                    continue
                event_id = int(event_id_el.text)
                if event_id in (1, 4688, 4698):
                    data_elements = root.findall(".//ns:Data", ns)
                    event_data = {}
                    for d in data_elements:
                        name = d.get("Name", "")
                        event_data[name] = d.text or ""
                    events.append({
                        "event_id": event_id,
                        "timestamp": record.timestamp().isoformat(),
                        "data": event_data,
                    })
            except ET.ParseError:
                continue
    return events


def scan_command_line(cmd_line):
    """Check a command line string against shadow copy deletion patterns."""
    findings = []
    for pattern in SHADOW_PATTERNS:
        if re.search(pattern, cmd_line, re.IGNORECASE):
            findings.append({"pattern": pattern, "severity": "CRITICAL", "category": "shadow_copy_deletion"})
    for pattern in RECOVERY_DISABLE_PATTERNS:
        if re.search(pattern, cmd_line, re.IGNORECASE):
            findings.append({"pattern": pattern, "severity": "HIGH", "category": "recovery_disable"})
    return findings


def hunt_evtx(evtx_path):
    """Hunt for shadow copy deletion in EVTX logs."""
    events = parse_evtx_file(evtx_path)
    results = []
    for event in events:
        cmd = event["data"].get("CommandLine", "") or event["data"].get("TaskContent", "")
        if not cmd:
            cmd = " ".join(event["data"].values())
        matches = scan_command_line(cmd)
        if matches:
            results.append({
                "timestamp": event["timestamp"],
                "event_id": event["event_id"],
                "command_line": cmd[:500],
                "user": event["data"].get("SubjectUserName", event["data"].get("User", "")),
                "computer": event["data"].get("Computer", ""),
                "findings": matches,
            })
    return results


def scan_sysmon_json(log_path):
    """Scan JSON-exported Sysmon logs for shadow copy deletion."""
    results = []
    with open(log_path) as f:
        for line in f:
            try:
                entry = json.loads(line)
            except json.JSONDecodeError:
                continue
            cmd = entry.get("CommandLine", entry.get("command_line", ""))
            image = entry.get("Image", entry.get("process_name", ""))
            matches = scan_command_line(cmd)
            if matches:
                results.append({
                    "timestamp": entry.get("UtcTime", entry.get("timestamp", "")),
                    "image": image,
                    "command_line": cmd[:500],
                    "parent_image": entry.get("ParentImage", ""),
                    "user": entry.get("User", ""),
                    "hostname": entry.get("Computer", entry.get("hostname", "")),
                    "findings": matches,
                })
    return results


def generate_sigma_rule():
    """Generate a Sigma detection rule for shadow copy deletion."""
    return {
        "title": "Shadow Copy Deletion via Vssadmin or WMIC",
        "id": "faa6e1e2-5b4c-4e1a-bb2a-5c1f3e5e3f0a",
        "status": "production",
        "level": "critical",
        "logsource": {"category": "process_creation", "product": "windows"},
        "detection": {
            "selection_vssadmin": {
                "Image|endswith": "\\vssadmin.exe",
                "CommandLine|contains|all": ["delete", "shadows"],
            },
            "selection_wmic": {
                "Image|endswith": "\\wmic.exe",
                "CommandLine|contains|all": ["shadowcopy", "delete"],
            },
            "selection_powershell": {
                "Image|endswith": ["\\powershell.exe", "\\pwsh.exe"],
                "CommandLine|contains": "Win32_ShadowCopy",
            },
            "condition": "selection_vssadmin or selection_wmic or selection_powershell",
        },
        "tags": ["attack.impact", "attack.t1490"],
    }


def main():
    parser = argparse.ArgumentParser(description="Shadow Copy Deletion Hunter")
    parser.add_argument("--evtx", help="Path to EVTX log file")
    parser.add_argument("--json-log", help="Path to JSON Sysmon log")
    parser.add_argument("--output", default="shadow_copy_hunt_report.json")
    parser.add_argument("--action", choices=["hunt_evtx", "hunt_json", "sigma", "full"],
                        default="full")
    args = parser.parse_args()

    report = {"generated_at": datetime.utcnow().isoformat(), "findings": {}}

    if args.action in ("hunt_evtx", "full") and args.evtx:
        results = hunt_evtx(args.evtx)
        report["findings"]["evtx_hits"] = results
        print(f"[+] EVTX shadow copy deletion events: {len(results)}")

    if args.action in ("hunt_json", "full") and args.json_log:
        results = scan_sysmon_json(args.json_log)
        report["findings"]["sysmon_hits"] = results
        print(f"[+] Sysmon JSON shadow copy hits: {len(results)}")

    if args.action in ("sigma", "full"):
        rule = generate_sigma_rule()
        report["findings"]["sigma_rule"] = rule
        print("[+] Sigma rule generated")

    with open(args.output, "w") as f:
        json.dump(report, f, indent=2, default=str)
    print(f"[+] Report saved to {args.output}")


if __name__ == "__main__":
    main()
process.py3.6 KB
Display-only source. This catalog never executes bundled scripts.
#!/usr/bin/env python3
"""Shadow Copy Deletion Detection - Analyzes logs for T1490 indicators."""

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

DETECTION_PATTERNS = [
    r'vssadmin.*delete',
    r'wmic.*shadowcopy.*delete',
    r'bcdedit.*recoveryenabled.*no',
    r'wbadmin.*delete',
    r'Get-WmiObject.*Win32_ShadowCopy.*Delete',
]

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": "T1490",
        "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"[*] Shadow Copy Deletion 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 = "hunting_for_shadow_c"
    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"# Shadow Copy Deletion 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="Shadow Copy Deletion 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="./hunting_for_sha_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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