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Analyzing Lnk File And Jump List Artifacts

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mukul975
analyzing-lnk-file-and-jump-list-artifacts

Analyze Windows LNK shortcut files and Jump List artifacts with LECmd, JLECmd, and manual Shell Link Binary Format parsing to establish evidence of file access, program execution, and user activity that persists even after the target file is deleted. Use when investigating Windows user activity, reconstructing file-access or program-execution timelines, or examining recent/frequently-used file evidence in a forensic exam.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameanalyzing-lnk-file-and-jump-list-artifacts
Stars
32.9K
Forks
4K
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Analyzing Lnk File And Jump List Artifacts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-lnk-file-and-jump-list-artifacts .claude/skills/analyzing-lnk-file-and-jump-list-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Lnk File And Jump List Artifacts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Lnk File And Jump List Artifacts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Lnk File And Jump List Artifacts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analyzing LNK File and Jump List Artifacts

Overview

Windows LNK (shortcut) files and Jump Lists are critical forensic artifacts that provide evidence of file access, program execution, and user behavior. LNK files are created automatically when a user opens a file through Windows Explorer or the Open/Save dialog, storing metadata about the target file including its original path, timestamps, volume serial number, NetBIOS name, and MAC address of the host system. Jump Lists, introduced in Windows 7, extend this by maintaining per-application lists of recently and frequently accessed files. These artifacts persist even after the target files are deleted, making them invaluable for establishing that a user accessed specific files at specific times.

When to Use

  • When investigating security incidents that require analyzing lnk file and jump list artifacts
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • LECmd (Eric Zimmerman) for LNK file parsing
  • JLECmd (Eric Zimmerman) for Jump List parsing
  • Python 3.8+ with pylnk3 or LnkParse3 libraries
  • Forensic image or triage collection from Windows system
  • Timeline Explorer for CSV analysis

LNK File Locations

LocationDescription
%USERPROFILE%\AppData\Roaming\Microsoft\Windows\Recent\Recent files accessed
%USERPROFILE%\Desktop\User-created shortcuts
%USERPROFILE%\AppData\Roaming\Microsoft\Windows\Start Menu\Start Menu shortcuts
%USERPROFILE%\AppData\Roaming\Microsoft\Office\Recent\Office recent documents

LNK File Structure

Shell Link Header (76 bytes)

OffsetSizeField
0x004HeaderSize (always 0x0000004C)
0x0416LinkCLSID (always 00021401-0000-0000-C000-000000000046)
0x144LinkFlags
0x184FileAttributes
0x1C8CreationTime (FILETIME)
0x248AccessTime (FILETIME)
0x2C8WriteTime (FILETIME)
0x344FileSize of target
0x384IconIndex
0x3C4ShowCommand
0x402HotKey

Key Forensic Fields in LNK Files

  • Target file timestamps: Creation, access, modification times of the referenced file
  • Volume information: Serial number, drive type, volume label
  • Network share information: UNC path, share name
  • Machine identifiers: NetBIOS name, MAC address (from TrackerDataBlock)
  • Distributed Link Tracking: Machine ID and object GUID

Analysis with EZ Tools

LECmd - LNK File Parser

powershell
# Parse all LNK files in Recent folder
LECmd.exe -d "C:\Evidence\Users\suspect\AppData\Roaming\Microsoft\Windows\Recent" --csv C:\Output --csvf lnk_analysis.csv

# Parse a single LNK file with full details
LECmd.exe -f "C:\Evidence\Users\suspect\Desktop\Confidential.docx.lnk" --json C:\Output

# Parse LNK files with additional detail levels
LECmd.exe -d "C:\Evidence\Users\suspect\AppData\Roaming\Microsoft\Windows\Recent" --csv C:\Output --csvf lnk_all.csv --all

JLECmd - Jump List Parser

powershell
# Parse Automatic Jump Lists
JLECmd.exe -d "C:\Evidence\Users\suspect\AppData\Roaming\Microsoft\Windows\Recent\AutomaticDestinations" --csv C:\Output --csvf jumplists_auto.csv

# Parse Custom Jump Lists
JLECmd.exe -d "C:\Evidence\Users\suspect\AppData\Roaming\Microsoft\Windows\Recent\CustomDestinations" --csv C:\Output --csvf jumplists_custom.csv

# Parse all jump lists with detailed output
JLECmd.exe -d "C:\Evidence\Users\suspect\AppData\Roaming\Microsoft\Windows\Recent\AutomaticDestinations" --csv C:\Output --csvf jumplists_auto.csv --ld

Jump List Structure

Automatic Destinations (automaticDestinations-ms)

These are OLE Compound files (Structured Storage) identified by AppID hash in the filename:

AppID HashApplication
5f7b5f1e01b83767Windows Explorer Pinned/Frequent
1b4dd67f29cb1962Windows Explorer Recent
9b9cdc69c1c24e2bNotepad
a7bd71699cd38d1cNotepad++
12dc1ea8e34b5a6Microsoft Paint
7e4dca80246863e3Control Panel
1cf97c38a5881255Microsoft Edge
f01b4d95cf55d32aWindows Explorer
9d1f905ce5044aeeMicrosoft Excel
a4a5324453625195Microsoft Word
d00655d2aa12ff6dMicrosoft PowerPoint
bc03160ee1a59fc1Outlook

Custom Destinations (customDestinations-ms)

Created when users pin items to application jump lists. These files contain sequential LNK entries.

Python Analysis Script

python
import struct
import os
from datetime import datetime, timedelta

FILETIME_EPOCH = datetime(1601, 1, 1)

def filetime_to_datetime(filetime_bytes: bytes) -> datetime:
    """Convert Windows FILETIME (100-ns intervals since 1601) to datetime."""
    ft = struct.unpack("<Q", filetime_bytes)[0]
    if ft == 0:
        return None
    return FILETIME_EPOCH + timedelta(microseconds=ft // 10)

def parse_lnk_header(lnk_path: str) -> dict:
    """Parse the Shell Link header from an LNK file."""
    with open(lnk_path, "rb") as f:
        header = f.read(76)

    header_size = struct.unpack("<I", header[0:4])[0]
    if header_size != 0x4C:
        return {"error": "Invalid LNK header"}

    link_flags = struct.unpack("<I", header[0x14:0x18])[0]
    file_attrs = struct.unpack("<I", header[0x18:0x1C])[0]

    result = {
        "header_size": header_size,
        "link_flags": hex(link_flags),
        "file_attributes": hex(file_attrs),
        "creation_time": filetime_to_datetime(header[0x1C:0x24]),
        "access_time": filetime_to_datetime(header[0x24:0x2C]),
        "write_time": filetime_to_datetime(header[0x2C:0x34]),
        "file_size": struct.unpack("<I", header[0x34:0x38])[0],
        "has_target_id_list": bool(link_flags & 0x01),
        "has_link_info": bool(link_flags & 0x02),
        "has_name": bool(link_flags & 0x04),
        "has_relative_path": bool(link_flags & 0x08),
        "has_working_dir": bool(link_flags & 0x10),
        "has_arguments": bool(link_flags & 0x20),
        "has_icon_location": bool(link_flags & 0x40),
    }
    return result

Investigation Use Cases

Evidence of File Access

  1. Parse LNK files from Recent folder to identify accessed documents
  2. Cross-reference with MFT timestamps and USN Journal entries
  3. Note that LNK files persist even after target files are deleted

Removable Media Access

  1. LNK files referencing drive letters E:, F:, G: indicate removable media usage
  2. Volume serial number in LNK identifies the specific device
  3. MAC address in TrackerDataBlock identifies the source machine

Network Share Activity

  1. LNK files with UNC paths (\server\share) indicate network file access
  2. NetBIOS name identifies the remote server
  3. Timestamps establish when access occurred

Differences Between Windows 10 and Windows 11

Recent research (IEEE 2025) shows that Windows 11 produces different LNK and Jump List artifacts:

  • Fewer automatic LNK files generated for certain file types
  • Modified Jump List behavior for modern applications
  • UWP/MSIX applications may not generate traditional Jump Lists
  • Windows 11 Quick Access replaces some Recent functionality

References

Example Output

text
$ LECmd.exe -d "C:\Evidence\Users\jsmith\AppData\Roaming\Microsoft\Windows\Recent" --csv /analysis/lnk_output

LECmd v1.11.0 - LNK File Parser
================================

Processing 47 LNK files...

--- LNK File: Q4_Report.xlsx.lnk ---
  Source:           C:\Evidence\Users\jsmith\Recent\Q4_Report.xlsx.lnk
  Target Path:      C:\Users\jsmith\Downloads\Q4_Report.xlsm
  Target Created:   2024-01-15 14:33:45 UTC
  Target Modified:  2024-01-15 14:33:45 UTC
  Target Accessed:  2024-01-15 14:35:12 UTC
  File Size:        251,904 bytes
  Drive Type:       Fixed (C:)
  Volume Serial:    A4E7-3F21
  Machine ID:       DESKTOP-J5M1TH
  MAC Address:      48:2A:E3:5C:9B:01

--- LNK File: update_client.exe.lnk ---
  Source:           C:\Evidence\Users\jsmith\Recent\update_client.exe.lnk
  Target Path:      C:\ProgramData\Updates\update_client.exe
  Target Created:   2024-01-15 14:34:02 UTC
  Target Modified:  2024-01-15 14:34:02 UTC
  Target Accessed:  2024-01-15 14:36:30 UTC
  File Size:        1,258,496 bytes
  Drive Type:       Fixed (C:)
  Volume Serial:    A4E7-3F21
  Machine ID:       DESKTOP-J5M1TH
  Working Dir:      C:\ProgramData\Updates
  Arguments:        --silent --no-update-check
  Run Window:       Hidden

======================================================================

$ JLECmd.exe -d "C:\Evidence\Users\jsmith\AppData\Roaming\Microsoft\Windows\Recent\AutomaticDestinations" --csv /analysis/jumplist_output

JLECmd v1.5.0 - Jump List Parser
==================================

Processing 23 AutomaticDestinations files...

--- Application: Microsoft Excel (AppID: 12dc1ea8e34b5a6) ---
  Entries: 15
  Most Recent:
    Entry 0:  C:\Users\jsmith\Downloads\Q4_Report.xlsm         (2024-01-15 14:35:12 UTC)
    Entry 1:  \\FILESERV01\Finance\Budget_2024.xlsx             (2024-01-14 09:22:30 UTC)
    Entry 2:  C:\Users\jsmith\Documents\Expenses\Dec2023.xlsx   (2024-01-10 16:45:00 UTC)

--- Application: Windows Explorer (AppID: f01b4d95cf55d32a) ---
  Entries: 28
  Most Recent:
    Entry 0:  C:\ProgramData\Updates\                           (2024-01-15 14:36:25 UTC)
    Entry 1:  E:\Backup\                                        (2024-01-15 15:30:00 UTC)
    Entry 2:  \\FILESERV01\HR\Employees\                        (2024-01-15 16:12:45 UTC)

--- Application: cmd.exe (AppID: 9b9cdc69c1c24e2b) ---
  Entries: 5
  Most Recent:
    Entry 0:  C:\Windows\System32\cmd.exe                       (2024-01-15 14:36:00 UTC)

Summary:
  Total LNK files processed:    47
  Total Jump List entries:       156
  Suspicious artifacts:          3 (hidden window execution, USB drive access, network shares)
  CSV exported to:               /analysis/lnk_output/ and /analysis/jumplist_output/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Lnk File And Jump List Artifacts AI skill do?

Analyze Windows LNK shortcut files and Jump List artifacts with LECmd, JLECmd, and manual Shell Link Binary Format parsing to establish evidence of file access, program execution, and user activity that persists even after the target file is deleted. Use when investigating Windows user activity, reconstructing file-access or program-execution timelines, or examining recent/frequently-used file evidence in a forensic exam.

Why use Analyzing Lnk File And Jump List Artifacts on TypingMind?

Because you install it once and use it with any model. Analyzing Lnk File And Jump List Artifacts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Lnk File And Jump List Artifacts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-lnk-file-and-jump-list-artifacts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Lnk File And Jump List Artifacts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Lnk File And Jump List Artifacts?

As many as you like. As long as a model supports skills, you can use Analyzing Lnk File And Jump List Artifacts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Lnk File And Jump List Artifacts AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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