analyzing-windows-registry-for-artifacts

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

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-windows-registry-for-artifacts
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summary

Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.

skill.md
name
analyzing-windows-registry-for-artifacts
description
Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
domain
cybersecurity
subdomain
digital-forensics
tags
- forensics - windows-registry - artifact-analysis - regripper - registry-explorer - evidence-collection
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01

Analyzing Windows Registry for Artifacts

When to Use

  • When investigating user activity on a Windows system during an incident
  • For identifying autorun/persistence mechanisms used by malware
  • When tracing installed software, USB devices, and network connections
  • During insider threat investigations to reconstruct user actions
  • For correlating registry timestamps with other forensic artifacts

Prerequisites

  • Forensic image or extracted registry hive files
  • RegRipper, Registry Explorer (Eric Zimmerman), or python-registry
  • Access to registry hive locations (SAM, SYSTEM, SOFTWARE, NTUSER.DAT, UsrClass.dat)
  • Understanding of Windows Registry structure (hives, keys, values)
  • SIFT Workstation or forensic analysis environment

Workflow

Step 1: Extract Registry Hives from the Forensic Image

# Mount the forensic image read-only
mkdir /mnt/evidence
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

# Copy system registry hives
cp /mnt/evidence/Windows/System32/config/SAM /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SYSTEM /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SOFTWARE /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/SECURITY /cases/case-2024-001/registry/
cp /mnt/evidence/Windows/System32/config/DEFAULT /cases/case-2024-001/registry/

# Copy user-specific hives
cp /mnt/evidence/Users/*/NTUSER.DAT /cases/case-2024-001/registry/
cp /mnt/evidence/Users/*/AppData/Local/Microsoft/Windows/UsrClass.dat /cases/case-2024-001/registry/

# Copy transaction logs (for dirty hive recovery)
cp /mnt/evidence/Windows/System32/config/*.LOG* /cases/case-2024-001/registry/logs/

# Hash all extracted hives
sha256sum /cases/case-2024-001/registry/* > /cases/case-2024-001/registry/hive_hashes.txt

Step 2: Analyze with RegRipper for Automated Artifact Extraction

# Install RegRipper
git clone https://github.com/keydet89/RegRipper3.0.git /opt/regripper

# Run RegRipper against NTUSER.DAT (user profile)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -f ntuser > /cases/case-2024-001/analysis/ntuser_report.txt

# Run against SYSTEM hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -f system > /cases/case-2024-001/analysis/system_report.txt

# Run against SOFTWARE hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -f software > /cases/case-2024-001/analysis/software_report.txt

# Run against SAM hive (user accounts)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SAM \
   -f sam > /cases/case-2024-001/analysis/sam_report.txt

# Run specific plugins
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p userassist > /cases/case-2024-001/analysis/userassist.txt

perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p usbstor > /cases/case-2024-001/analysis/usbstor.txt

Step 3: Extract Persistence and Autorun Entries

# Using python-registry for targeted extraction
pip install python-registry

python3 << 'PYEOF'
from Registry import Registry

# Open SOFTWARE hive
reg = Registry.Registry("/cases/case-2024-001/registry/SOFTWARE")

# Check Run keys (autostart)
autorun_paths = [
    "Microsoft\\Windows\\CurrentVersion\\Run",
    "Microsoft\\Windows\\CurrentVersion\\RunOnce",
    "Microsoft\\Windows\\CurrentVersion\\RunServices",
    "Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\Run",
    "Wow6432Node\\Microsoft\\Windows\\CurrentVersion\\Run"
]

for path in autorun_paths:
    try:
        key = reg.open(path)
        print(f"\n=== {path} (Last Modified: {key.timestamp()}) ===")
        for value in key.values():
            print(f"  {value.name()}: {value.value()}")
    except Registry.RegistryKeyNotFoundException:
        pass

# Check installed services
key = reg.open("Microsoft\\Windows NT\\CurrentVersion\\Svchost")
print(f"\n=== Svchost Groups ===")
for value in key.values():
    print(f"  {value.name()}: {value.value()}")
PYEOF

# Check NTUSER.DAT for user-specific autorun
python3 << 'PYEOF'
from Registry import Registry

reg = Registry.Registry("/cases/case-2024-001/registry/NTUSER.DAT")

user_autorun = [
    "Software\\Microsoft\\Windows\\CurrentVersion\\Run",
    "Software\\Microsoft\\Windows\\CurrentVersion\\RunOnce",
    "Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run"
]

for path in user_autorun:
    try:
        key = reg.open(path)
        print(f"\n=== {path} (Last Modified: {key.timestamp()}) ===")
        for value in key.values():
            print(f"  {value.name()}: {value.value()}")
    except Registry.RegistryKeyNotFoundException:
        pass
PYEOF

Step 4: Analyze User Activity Artifacts

# Extract UserAssist data (program execution history with ROT13 encoding)
python3 << 'PYEOF'
from Registry import Registry
import codecs, struct, datetime

reg = Registry.Registry("/cases/case-2024-001/registry/NTUSER.DAT")

ua_path = "Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\UserAssist"
key = reg.open(ua_path)

for guid_key in key.subkeys():
    count_key = guid_key.subkey("Count")
    print(f"\n=== {guid_key.name()} ===")
    for value in count_key.values():
        decoded_name = codecs.decode(value.name(), 'rot_13')
        data = value.value()
        if len(data) >= 16:
            run_count = struct.unpack('<I', data[4:8])[0]
            focus_count = struct.unpack('<I', data[8:12])[0]
            timestamp = struct.unpack('<Q', data[60:68])[0] if len(data) >= 68 else 0
            if timestamp > 0:
                ts = datetime.datetime(1601,1,1) + datetime.timedelta(microseconds=timestamp//10)
                print(f"  {decoded_name}: Runs={run_count}, Focus={focus_count}, Last={ts}")
            else:
                print(f"  {decoded_name}: Runs={run_count}, Focus={focus_count}")
PYEOF

# Extract Recent Documents (MRU lists)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p recentdocs > /cases/case-2024-001/analysis/recentdocs.txt

# Extract typed URLs (browser)
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p typedurls > /cases/case-2024-001/analysis/typedurls.txt

# Extract typed paths in Explorer
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/NTUSER.DAT \
   -p typedpaths > /cases/case-2024-001/analysis/typedpaths.txt

Step 5: Extract System and Network Information

# Computer name and OS version from SYSTEM hive
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p compname > /cases/case-2024-001/analysis/system_info.txt

# Network interfaces and configuration
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p nic2 >> /cases/case-2024-001/analysis/system_info.txt

# Wireless network history
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -p networklist > /cases/case-2024-001/analysis/network_history.txt

# Timezone configuration
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p timezone > /cases/case-2024-001/analysis/timezone.txt

# Shutdown time
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SYSTEM \
   -p shutdown > /cases/case-2024-001/analysis/shutdown.txt

# Installed software from Uninstall keys
perl /opt/regripper/rip.pl -r /cases/case-2024-001/registry/SOFTWARE \
   -p uninstall > /cases/case-2024-001/analysis/installed_software.txt

Key Concepts

ConceptDescription
Registry hiveBinary file storing a section of the registry (SAM, SYSTEM, SOFTWARE, NTUSER.DAT)
MRU (Most Recently Used)Lists tracking recently accessed files, commands, and search terms
UserAssistROT13-encoded registry entries tracking program execution with timestamps
ShimCacheApplication compatibility cache recording executed programs
AmCacheDetailed execution history including SHA-1 hashes of executables
BAM/DAMBackground/Desktop Activity Moderator tracking program execution in Win10+
Last Write TimeTimestamp on registry keys indicating when they were last modified
Transaction logsJournal files allowing recovery of registry state after improper shutdown

Tools & Systems

ToolPurpose
RegRipperAutomated registry artifact extraction with plugin architecture
Registry ExplorerEric Zimmerman GUI tool for interactive registry analysis
python-registryPython library for programmatic registry hive parsing
RECmdEric Zimmerman command-line registry analysis tool
yarpYet Another Registry Parser for Python-based analysis
AppCompatCacheParserDedicated ShimCache/AppCompatCache parser
AmcacheParserDedicated AmCache.hve analysis tool
ShellBags ExplorerSpecialized tool for analyzing ShellBag artifacts

Common Scenarios

Scenario 1: Malware Persistence Investigation Extract SOFTWARE and NTUSER.DAT hives, check all Run/RunOnce keys for unauthorized entries, examine services for suspicious additions, check scheduled tasks registry keys, correlate autorun timestamps with malware execution timeline.

Scenario 2: User Activity Reconstruction Analyze UserAssist for program execution history, examine RecentDocs for accessed files, check TypedPaths for Explorer navigation, extract ShellBags for folder access patterns, build a timeline of user activity around the incident window.

Scenario 3: Unauthorized Software Detection Parse Uninstall keys for all installed applications, compare against approved software baseline, check BAM/DAM for recently executed programs not in approved list, examine AppCompatCache for execution evidence even after uninstallation.

Scenario 4: USB Data Exfiltration Investigation Extract USBSTOR entries from SYSTEM hive for connected devices, correlate device serial numbers with MountedDevices, check NTUSER.DAT MountPoints2 for user access to removable media, examine SetupAPI logs for first-connection timestamps.

Output Format

Registry Analysis Summary:
  System: DESKTOP-ABC123 (Windows 10 Pro Build 19041)
  Timezone: Eastern Standard Time (UTC-5)
  Last Shutdown: 2024-01-18 23:45:12 UTC

  Autorun Entries:
    HKLM Run:     5 entries (1 suspicious: "updater.exe" -> C:\ProgramData\svc\updater.exe)
    HKCU Run:     3 entries (all legitimate)
    Services:     142 entries (2 unknown: "WinDefSvc", "SysMonAgent")

  User Activity (NTUSER.DAT):
    UserAssist Programs:  234 entries
    Recent Documents:     89 entries
    Typed URLs:           45 entries
    Typed Paths:          12 entries

  USB Devices Connected:
    - Kingston DataTraveler (Serial: 0019E06B4521) - First: 2024-01-10, Last: 2024-01-18
    - WD My Passport (Serial: 575834314131) - First: 2024-01-15, Last: 2024-01-15

  Installed Software:     127 applications
  Suspicious Findings:    3 items flagged for review
how to use analyzing-windows-registry-for-artifacts

How to use analyzing-windows-registry-for-artifacts on Cursor

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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 analyzing-windows-registry-for-artifacts
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/analyzing-windows-registry-for-artifacts

The skills CLI fetches analyzing-windows-registry-for-artifacts 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?
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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analyzing-windows-registry-for-artifacts

Reload or restart Cursor to activate analyzing-windows-registry-for-artifacts. Access the skill through slash commands (e.g., /analyzing-windows-registry-for-artifacts) 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.

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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

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general reviews

Ratings

4.648 reviews
  • Diya Abbas· Dec 24, 2024

    analyzing-windows-registry-for-artifacts fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ganesh Mohane· Dec 20, 2024

    analyzing-windows-registry-for-artifacts reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hana Zhang· Dec 20, 2024

    Keeps context tight: analyzing-windows-registry-for-artifacts is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mia Flores· Dec 4, 2024

    I recommend analyzing-windows-registry-for-artifacts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Sanchez· Nov 27, 2024

    Solid pick for teams standardizing on skills: analyzing-windows-registry-for-artifacts is focused, and the summary matches what you get after install.

  • Lucas Ramirez· Nov 23, 2024

    analyzing-windows-registry-for-artifacts reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Diya Ramirez· Nov 15, 2024

    analyzing-windows-registry-for-artifacts is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Nov 11, 2024

    I recommend analyzing-windows-registry-for-artifacts for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Lucas Torres· Nov 11, 2024

    We added analyzing-windows-registry-for-artifacts from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Lucas Sanchez· Oct 18, 2024

    analyzing-windows-registry-for-artifacts has been reliable in day-to-day use. Documentation quality is above average for community skills.

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