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.
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| name | analyzing-windows-prefetch-with-python |
| description | 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. |
| domain | cybersecurity |
| subdomain | digital-forensics |
| tags | - digital-forensics - windows - prefetch - execution-history - incident-response - malware-analysis |
| mitre_attack | - T1059 - T1204 - T1036 |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
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.
windowsprefetch library (pip install windowsprefetch)Gather .pf files from target system's C:\Windows\Prefetch\ directory.
Extract executable name, run count, last execution timestamps, and volume information.
Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.
Reconstruct chronological execution timeline from all Prefetch files.
JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.
$ 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
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
analyzing-windows-prefetch-with-python reduced setup friction for our internal harness; good balance of opinion and flexibility.
analyzing-windows-prefetch-with-python is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
analyzing-windows-prefetch-with-python has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: analyzing-windows-prefetch-with-python is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in analyzing-windows-prefetch-with-python — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
analyzing-windows-prefetch-with-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend analyzing-windows-prefetch-with-python for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for analyzing-windows-prefetch-with-python matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for analyzing-windows-prefetch-with-python matched our evaluation — installs cleanly and behaves as described in the markdown.
analyzing-windows-prefetch-with-python reduced setup friction for our internal harness; good balance of opinion and flexibility.
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