analyzing-windows-registry-for-artifacts▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
| 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
| Concept | Description |
|---|---|
| Registry hive | Binary 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 |
| UserAssist | ROT13-encoded registry entries tracking program execution with timestamps |
| ShimCache | Application compatibility cache recording executed programs |
| AmCache | Detailed execution history including SHA-1 hashes of executables |
| BAM/DAM | Background/Desktop Activity Moderator tracking program execution in Win10+ |
| Last Write Time | Timestamp on registry keys indicating when they were last modified |
| Transaction logs | Journal files allowing recovery of registry state after improper shutdown |
Tools & Systems
| Tool | Purpose |
|---|---|
| RegRipper | Automated registry artifact extraction with plugin architecture |
| Registry Explorer | Eric Zimmerman GUI tool for interactive registry analysis |
| python-registry | Python library for programmatic registry hive parsing |
| RECmd | Eric Zimmerman command-line registry analysis tool |
| yarp | Yet Another Registry Parser for Python-based analysis |
| AppCompatCacheParser | Dedicated ShimCache/AppCompatCache parser |
| AmcacheParser | Dedicated AmCache.hve analysis tool |
| ShellBags Explorer | Specialized 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 on Cursor
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Prerequisites
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- ›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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches analyzing-windows-registry-for-artifacts from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
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Verify installation
Confirm successful installation by checking the skill directory location:
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
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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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
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Ratings
4.6★★★★★48 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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