Analyzing Windows Lnk Files For ArtifactsSAFE
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
Overview
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
6c59587be632OBSERVED · 2026-10-07Install
Commands as the repository documents them. They are shown, not run.
pip install LnkParse3
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: analyzing-windows-lnk-files-for-artifacts
description: Parse Windows LNK shortcut files to extract target paths, MAC timestamps,
volume serial numbers, and machine identifiers for forensic timeline reconstruction.
Use when investigating recently-accessed files, tracking removable media or network
paths referenced by shortcuts, or building a DFIR timeline from LNK artifacts.
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-03
- DE.AE-02
- RS.MA-01
mitre_attack:
- T1547.001
- T1204.002
- T1005
- T1025
- T1074.001
---
# 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
```bash
# 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
```bash
# 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
```bash
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
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
6c59587be632full audit observations/trust-audit/skill/mukul975__analyzing-windows-lnk-files-for-artifacts.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-07 | 6c59587be632 | SAFE | B | 89 | first audit |
Questions
What does the Analyzing Windows Lnk Files For Artifacts skill do?
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
Is Analyzing Windows Lnk Files For Artifacts safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Analyzing Windows Lnk Files For Artifacts access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (6c59587be632), read on 2026-10-07. The repository is watched, and a new audit runs when it changes — this is the first audit.