analyzing-windows-lnk-files-for-artifacts

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

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-windows-lnk-files-for-artifacts
0 commentsdiscussion
summary

Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.

skill.md
name
analyzing-windows-lnk-files-for-artifacts
description
Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.
domain
cybersecurity
subdomain
digital-forensics
tags
- forensics - lnk-files - windows-artifacts - shortcut-analysis - timeline-reconstruction - 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 LNK Files for Artifacts

When to Use

  • When reconstructing user file access history from Windows shortcut files
  • For tracking accessed files, network shares, and removable media
  • During investigations to prove a user opened specific documents
  • When correlating file access with other timeline artifacts
  • For identifying accessed paths on remote systems or USB devices

Prerequisites

  • Access to LNK files from forensic image (Recent, Desktop, Quick Launch)
  • LECmd (Eric Zimmerman), python-lnk, or LnkParser for analysis
  • Understanding of LNK file structure (Shell Link Binary format)
  • Knowledge of LNK file locations on Windows systems
  • Forensic workstation with analysis tools installed

Workflow

Step 1: Collect LNK Files from Forensic Image

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

mkdir -p /cases/case-2024-001/lnk/{recent,desktop,startup,custom}

# Copy Recent items LNK files (primary source)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/*.lnk \
   /cases/case-2024-001/lnk/recent/ 2>/dev/null

# Copy automatic destinations (Jump Lists)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/AutomaticDestinations/*.automaticDestinations-ms \
   /cases/case-2024-001/lnk/recent/ 2>/dev/null

# Copy custom destinations (pinned Jump List items)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Recent/CustomDestinations/*.customDestinations-ms \
   /cases/case-2024-001/lnk/custom/ 2>/dev/null

# Copy Desktop shortcuts
cp /mnt/evidence/Users/*/Desktop/*.lnk /cases/case-2024-001/lnk/desktop/ 2>/dev/null

# Copy Startup folder shortcuts (persistence)
cp /mnt/evidence/Users/*/AppData/Roaming/Microsoft/Windows/Start\ Menu/Programs/Startup/*.lnk \
   /cases/case-2024-001/lnk/startup/ 2>/dev/null
cp "/mnt/evidence/ProgramData/Microsoft/Windows/Start Menu/Programs/Startup"/*.lnk \
   /cases/case-2024-001/lnk/startup/ 2>/dev/null

# Find all LNK files on the system
find /mnt/evidence/ -name "*.lnk" -type f 2>/dev/null > /cases/case-2024-001/lnk/all_lnk_locations.txt

# Count and hash
ls /cases/case-2024-001/lnk/recent/ | wc -l
sha256sum /cases/case-2024-001/lnk/recent/*.lnk > /cases/case-2024-001/lnk/lnk_hashes.txt 2>/dev/null

Step 2: Parse LNK Files with LECmd

# Using Eric Zimmerman's LECmd (Windows or via Mono)
# Process all LNK files in a directory
LECmd.exe -d "C:\cases\lnk\recent\" --csv "C:\cases\analysis\" --csvf lnk_analysis.csv

# Process a single LNK file with verbose output
LECmd.exe -f "C:\cases\lnk\recent\document.pdf.lnk"

# Process Jump List files
JLECmd.exe -d "C:\cases\lnk\recent\" --csv "C:\cases\analysis\" --csvf jumplist_analysis.csv

# Output includes:
# - Source file path
# - Target path (file that was accessed)
# - Target creation, modification, access timestamps
# - LNK creation and modification timestamps
# - Working directory
# - Command line arguments
# - Volume serial number and label
# - Drive type (Fixed, Removable, Network)
# - Machine ID (NetBIOS name)
# - MAC address (from tracker database)
# - File size of target

Step 3: Parse LNK Files with Python

pip install LnkParse3

python3 << 'PYEOF'
import LnkParse3
import os, json, csv
from datetime import datetime

lnk_dir = '/cases/case-2024-001/lnk/recent/'
results = []

for filename in sorted(os.listdir(lnk_dir)):
    if not filename.lower().endswith('.lnk'):
        continue

    filepath = os.path.join(lnk_dir, filename)
    try:
        with open(filepath, 'rb') as f:
            lnk = LnkParse3.lnk_file(f)
            info = lnk.get_json()

            parsed = {
                'lnk_file': filename,
                'target_path': '',
                'working_dir': '',
                'arguments': '',
                'target_created': '',
                'target_modified': '',
                'target_accessed': '',
                'file_size': '',
                'drive_type': '',
                'volume_serial': '',
                'volume_label': '',
                'machine_id': '',
                'mac_address': '',
            }

            # Extract header timestamps
            header = info.get('header', {})
            parsed['target_created'] = str(header.get('creation_time', ''))
            parsed['target_modified'] = str(header.get('modified_time', ''))
            parsed['target_accessed'] = str(header.get('accessed_time', ''))
            parsed['file_size'] = str(header.get('file_size', ''))

            # Extract link info
            link_info = info.get('link_info', {})
            if link_info:
                local_path = link_info.get('local_base_path', '')
                network_path = link_info.get('common_network_relative_link', {}).get('net_name', '')
                parsed['target_path'] = local_path or network_path

                vol_info = link_info.get('volume_id', {})
                if vol_info:
                    parsed['drive_type'] = str(vol_info.get('drive_type', ''))
                    parsed['volume_serial'] = str(vol_info.get('drive_serial_number', ''))
                    parsed['volume_label'] = str(vol_info.get('volume_label', ''))

            # Extract string data
            string_data = info.get('string_data', {})
            parsed['working_dir'] = str(string_data.get('working_dir', ''))
            parsed['arguments'] = str(string_data.get('command_line_arguments', ''))

            # Extract tracker data (machine ID and MAC)
            extra = info.get('extra', {})
            tracker = extra.get('DISTRIBUTED_LINK_TRACKER_BLOCK', {})
            if tracker:
                parsed['machine_id'] = str(tracker.get('machine_id', ''))
                parsed['mac_address'] = str(tracker.get('mac_address', ''))

            results.append(parsed)

            # Print summary
            print(f"\n{filename}")
            print(f"  Target: {parsed['target_path']}")
            print(f"  Modified: {parsed['target_modified']}")
            print(f"  Drive: {parsed['drive_type']} (Serial: {parsed['volume_serial']})")
            if parsed['machine_id']:
                print(f"  Machine: {parsed['machine_id']}")

    except Exception as e:
        print(f"  Error parsing {filename}: {e}")

# Write results to CSV
with open('/cases/case-2024-001/analysis/lnk_analysis.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=results[0].keys() if results else [])
    writer.writeheader()
    writer.writerows(results)

print(f"\n\nTotal LNK files parsed: {len(results)}")
PYEOF

Step 4: Analyze for Investigative Value

# Identify files accessed from removable media
python3 << 'PYEOF'
import csv

with open('/cases/case-2024-001/analysis/lnk_analysis.csv') as f:
    reader = csv.DictReader(f)

    print("=== FILES ACCESSED FROM REMOVABLE MEDIA ===\n")
    removable = []
    network = []

    for row in reader:
        if 'DRIVE_REMOVABLE' in row.get('drive_type', '').upper() or \
           'removable' in row.get('drive_type', '').lower():
            removable.append(row)
            print(f"  {row['target_modified']} | {row['target_path']} | Vol: {row['volume_serial']}")

        if 'network' in row.get('drive_type', '').lower() or \
           row.get('target_path', '').startswith('\\\\'):
            network.append(row)

    print(f"\n=== FILES ACCESSED FROM NETWORK SHARES ===\n")
    for row in network:
        print(f"  {row['target_modified']} | {row['target_path']}")

    print(f"\nRemovable media files: {len(removable)}")
    print(f"Network share files: {len(network)}")

    # Check for unique machines (tracker data)
    machines = set()
    for row in [*removable, *network]:
        if row.get('machine_id'):
            machines.add(row['machine_id'])
    if machines:
        print(f"\nMachine IDs found: {machines}")
PYEOF

# Check Startup folder LNK files for persistence
echo "=== STARTUP FOLDER SHORTCUTS (PERSISTENCE) ===" > /cases/case-2024-001/analysis/startup_persistence.txt
for lnk in /cases/case-2024-001/lnk/startup/*.lnk; do
    python3 -c "
import LnkParse3
with open('$lnk', 'rb') as f:
    lnk = LnkParse3.lnk_file(f)
    info = lnk.get_json()
    target = info.get('link_info', {}).get('local_base_path', 'Unknown')
    args = info.get('string_data', {}).get('command_line_arguments', '')
    print(f'  $(basename $lnk): {target} {args}')
" >> /cases/case-2024-001/analysis/startup_persistence.txt 2>/dev/null
done

Key Concepts

ConceptDescription
Shell Link (.lnk)Windows shortcut file format containing target path, timestamps, and metadata
Target timestampsCreation, modification, and access times of the file the shortcut points to
Volume serial numberUnique identifier of the drive volume where the target file resides
Machine IDNetBIOS name embedded by the Distributed Link Tracking service
MAC addressNetwork adapter MAC from the machine that created the LNK file
Jump ListsRecent and pinned file lists per application (contain embedded LNK data)
Automatic DestinationsSystem-managed Jump List entries for recently opened files
Custom DestinationsUser-pinned Jump List items that persist until manually removed

Tools & Systems

ToolPurpose
LECmdEric Zimmerman command-line LNK file parser with CSV/JSON output
JLECmdEric Zimmerman Jump List parser
LnkParse3Python library for programmatic LNK file analysis
lnk_parserAlternative Python LNK parsing tool
AutopsyForensic platform with LNK file analysis module
KAPEAutomated LNK and Jump List artifact collection
PlasoTimeline tool with LNK file parser for super-timeline creation
LNK ExplorerGUI tool for interactive LNK file examination

Common Scenarios

Scenario 1: Data Exfiltration via USB Drive Analyze Recent folder LNK files for targets on removable drives, correlate volume serial numbers with USBSTOR registry entries, build a list of files accessed from USB devices, establish which documents were opened from the removable drive, correlate with file copy timestamps.

Scenario 2: Malware Persistence via Startup Shortcuts Examine Startup folder LNK files for malicious targets, check target path and arguments for encoded commands or suspicious executables, verify target file exists and examine it, correlate creation timestamp with initial compromise time.

Scenario 3: Network Share Access Investigation Filter LNK files with network paths (UNC targets), identify which network shares were accessed and when, correlate machine IDs with known corporate systems, check if sensitive file servers were accessed outside of normal duties, build access timeline for compliance investigation.

Scenario 4: Document Access Timeline for Legal Proceedings Extract all Recent folder LNK files, build chronological list of documents accessed by the user, identify specific files relevant to the case, present target timestamps showing when files were opened, correlate with email and communication timelines.

Output Format

LNK File Analysis Summary:
  User Profile: suspect_user
  Total LNK Files: 234 (Recent: 198, Desktop: 23, Startup: 5, Other: 8)

  File Access Statistics:
    Local drive (C:):    156 files
    Removable media:     23 files (3 unique volume serials)
    Network shares:      15 files (\\server01, \\fileserver)
    Other drives:        4 files

  Machine IDs Found: DESKTOP-ABC123, LAPTOP-XYZ789
  MAC Addresses: AA:BB:CC:DD:EE:FF, 11:22:33:44:55:66

  Removable Media Access:
    Volume Serial 1234-ABCD:
      2024-01-15 14:32 - E:\Confidential\financial_report.xlsx
      2024-01-15 14:45 - E:\Confidential\customer_database.csv
      2024-01-15 15:00 - E:\Projects\source_code.zip

  Startup Persistence:
    updater.lnk -> C:\ProgramData\svc\updater.exe (SUSPICIOUS)
    OneDrive.lnk -> C:\Users\...\OneDrive.exe (Legitimate)

  Timeline: /cases/case-2024-001/analysis/lnk_analysis.csv
how to use analyzing-windows-lnk-files-for-artifacts

How to use analyzing-windows-lnk-files-for-artifacts 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 analyzing-windows-lnk-files-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-lnk-files-for-artifacts

The skills CLI fetches analyzing-windows-lnk-files-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?
│ ── 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/analyzing-windows-lnk-files-for-artifacts

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

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

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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)
  • No comments yet — start the thread.
general reviews

Ratings

4.642 reviews
  • Amina Lopez· Dec 12, 2024

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

  • Meera Bansal· Dec 8, 2024

    Registry listing for analyzing-windows-lnk-files-for-artifacts matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Amina Jain· Dec 4, 2024

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

  • Aanya Martinez· Nov 27, 2024

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

  • Sakshi Patil· Nov 15, 2024

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

  • Zaid Smith· Nov 3, 2024

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

  • Aisha Ghosh· Oct 22, 2024

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

  • Diego Okafor· Oct 18, 2024

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

  • Chaitanya Patil· Oct 6, 2024

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

  • Advait Iyer· Sep 25, 2024

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

showing 1-10 of 42

1 / 5