Skip to content
All Skills

Analyzing Windows Prefetch With Python

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

Security & Compliance|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "analyzing-windows-prefetch-with-python-bf787b66" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
# Analyzing Windows Prefetch with Python

## Overview

Windows Prefetch files (.pf) record application execution data including executable names, run counts, timestamps, loaded DLLs, and accessed directories. This skill covers parsing Prefetch files using the windowsprefetch Python library to reconstruct execution timelines, detect renamed or masquerading binaries by comparing executable names with loaded resources, and identifying suspicious programs that may indicate malware execution or lateral movement.


## When to Use

- When investigating security incidents that require analyzing windows prefetch with python
- 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

- Python 3.9+ with `windowsprefetch` library (pip install windowsprefetch)
- Windows Prefetch files from C:\Windows\Prefetch\ (versions 17-30 supported)
- Understanding of Windows Prefetch file naming conventions (EXECUTABLE-HASH.pf)

## Steps

### Step 1: Collect Prefetch Files
Gather .pf files from target system's C:\Windows\Prefetch\ directory.

### Step 2: Parse Execution History
Extract executable name, run count, last execution timestamps, and volume information.

### Step 3: Detect Suspicious Execution
Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.

### Step 4: Build Execution Timeline
Reconstruct chronological execution timeline from all Prefetch files.

## Expected Output

JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.

## Example Output

```text
$ python3 prefetch_analyzer.py --dir /evidence/Windows/Prefetch --output /analysis/prefetch_report

Windows Prefetch Analyzer v2.1
================================
Source: /evidence/Windows/Prefetch/
Prefetch Format: Windows 10 (MAM compressed, version 30)
Files Found: 234

--- Execution Timeline (Incident Window: 2024-01-15 to 2024-01-18) ---
Last Executed (UTC)     | Run Count | Filename                    | Hash     | Path
------------------------|-----------|-----------------------------|----------|------------------------------------------
2024-01-15 14:33:15     | 1         | Q4_REPORT.XLSM-2A1B3C4D.pf | 2A1B3C4D | C:\Users\jsmith\Downloads\Q4_Report.xlsm
2024-01-15 14:35:44     | 1         | POWERSHELL.EXE-A2B3C4D5.pf  | A2B3C4D5 | C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe
2024-01-15 14:36:30     | 3         | UPDATE_CLIENT.EXE-B3C4D5E6.pf| B3C4D5E6| C:\ProgramData\Updates\update_client.exe
2024-01-15 15:10:22     | 1         | NETSCAN.EXE-C4D5E6F7.pf     | C4D5E6F7 | C:\Users\jsmith\Downloads\netscan.exe
2024-01-16 02:28:00     | 1         | PROCDUMP64.EXE-D5E6F7A8.pf  | D5E6F7A8 | C:\Windows\Temp\procdump64.exe
2024-01-16 02:30:15     | 2         | MIMIKATZ.EXE-E6F7A8B9.pf    | E6F7A8B9 | C:\Windows\Temp\mimikatz.exe
2024-01-16 02:40:00     | 4         | PSEXEC.EXE-F7A8B9C0.pf      | F7A8B9C0 | C:\Users\jsmith\AppData\Local\Temp\psexec.exe
2024-01-17 02:45:00     | 1         | SDELETE64.EXE-A8B9C0D1.pf   | A8B9C0D1 | C:\Windows\Temp\sdelete64.exe
2024-01-18 03:00:45     | 1         | WEVTUTIL.EXE-B9C0D1E2.pf    | B9C0D1E2 | C:\Windows\System32\wevtutil.exe

--- Renamed Binary Detection ---
ALERT: UPDATE_CLIENT.EXE loaded DLLs consistent with Cobalt Strike beacon:
  Referenced DLLs: wininet.dll, ws2_32.dll, advapi32.dll, dnsapi.dll, netapi32.dll
  Volume: \VOLUME{01d94f2a3b5c7d8e-A4E73F21} (C:)
  Directories referenced:
    C:\ProgramData\Updates\
    C:\Windows\System32\

--- Execution Frequency Analysis ---
Most Executed (Top 5):
  1. SVCHOST.EXE          (267 runs)
  2. CHROME.EXE           (189 runs)
  3. EXPLORER.EXE         (156 runs)
  4. RUNTIMEBROKER.EXE    (134 runs)
  5. OUTLOOK.EXE          (98 runs)

First-Time Executions (Never seen before incident window):
  6 executables first run between 2024-01-15 and 2024-01-18

Summary:
  Total prefetch files:         234
  Suspicious executables:       6
  Renamed binary indicators:    1 (update_client.exe)
  Anti-forensics tools:         2 (sdelete64.exe, wevtutil.exe)
  JSON report: /analysis/prefetch_report/prefetch_timeline.json
```
#mukul-cybersecurity-skills#security#cybersecurity#forensicspython

Related Skills

More skills in Security & Compliance

1password

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.

#broad-capability#developmentMIT

1password

Set up and use 1Password CLI for sign-in, desktop integration, and reading or injecting secrets.

#identity#accessMIT

Accessibility Lead

Accessibility team lead and orchestrator. Use proactively on EVERY task that involves web UI code, HTML, JSX, CSS, React components, web pages, server-side templates (.leaf, .ejs, .erb, .hbs), or any user-facing web content. This agent coordinates the accessibility specialist team and ensures no accessibility requirement is missed. Runs the final review before any UI code is considered complete. Applies to any web framework, server-side templating framework (Vapor/Leaf, Rails/ERB, Django/Jinja, Express/EJS), or vanilla HTML/CSS/JS. Works alongside other team leads (e.g., swift-lead) in multi-language projects.

#broad-capability#accessibilityMIT

Accessibility Regression Detector

Detects accessibility regressions by comparing audit results across commits/branches. Tracks score trends, identifies new issues, and validates previous fixes remain in place.

#broad-capability#accessibilityMIT

Accessibility Statement

Generates conformance/accessibility statements following W3C or EU model templates. Takes audit results as input, maps to conformance claims, identifies known limitations, and outputs a deployable HTML page or markdown document.

#broad-capability#accessibilityMIT

Accessibility Tool Builder

Expert in building accessibility scanning tools, rule engines, document parsers, report generators, and audit automation. WCAG criterion mapping, severity scoring, CLI/GUI scanner architecture, CI/CD integration.

#broad-capability#accessibilityMIT