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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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feat: add 5 cybersecurity skills - CloudTrail anomalies, SSL/TLS assessment, Wazuh detection, Prefetch analysis, WMI lateral movement
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mukul975
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MIT License
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Copyright (c) 2025 Mahipal
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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---
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name: analyzing-windows-prefetch-with-python
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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.
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domain: cybersecurity
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subdomain: digital-forensics
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tags: [digital-forensics, windows, prefetch, execution-history, incident-response, malware-analysis]
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version: "1.0"
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author: mahipal
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license: MIT
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---
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# Analyzing Windows Prefetch with Python
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## Overview
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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.
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## Prerequisites
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- Python 3.9+ with `windowsprefetch` library (pip install windowsprefetch)
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- Windows Prefetch files from C:\Windows\Prefetch\ (versions 17-30 supported)
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- Understanding of Windows Prefetch file naming conventions (EXECUTABLE-HASH.pf)
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## Steps
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### Step 1: Collect Prefetch Files
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Gather .pf files from target system's C:\Windows\Prefetch\ directory.
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### Step 2: Parse Execution History
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Extract executable name, run count, last execution timestamps, and volume information.
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### Step 3: Detect Suspicious Execution
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Flag known attack tools (mimikatz, psexec, etc.), renamed binaries, and unusual execution patterns.
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### Step 4: Build Execution Timeline
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Reconstruct chronological execution timeline from all Prefetch files.
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## Expected Output
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JSON report with execution history, suspicious executables, renamed binary indicators, and timeline reconstruction.
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# API Reference: Analyzing Windows Prefetch with Python
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## windowsprefetch Library
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```python
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import windowsprefetch
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pf = windowsprefetch.Prefetch("CMD.EXE-1234ABCD.pf")
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print(pf.executableName) # CMD.EXE
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print(pf.runCount) # 42
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print(pf.lastRunTime) # 2025-01-15 10:30:22
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print(pf.timestamps) # List of up to 8 execution times
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print(pf.resources) # List of loaded files/DLLs
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print(pf.volumes) # Volume info (name, serial, creation)
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```
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Install: `pip install windowsprefetch`
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## Prefetch File Versions
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| Version | Windows | Max Timestamps |
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|---------|---------|----------------|
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| 17 | XP/2003 | 1 |
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| 23 | Vista/7 | 1 |
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| 26 | 8/8.1 | 8 |
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| 30 | 10/11 | 8 (compressed) |
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## File Naming Convention
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Format: `EXECUTABLE-XXXXXXXX.pf`
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- EXECUTABLE: uppercase executable name
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- XXXXXXXX: hash of file path (allows multiple entries per executable)
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## Suspicious Executables to Flag
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| Category | Examples |
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|----------|---------|
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| Credential tools | mimikatz, rubeus, lazagne, secretsdump |
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| Lateral movement | psexec, psexesvc, wmiexec |
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| C2 agents | beacon, meterpreter, covenant, empire |
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| LOLBins | certutil, mshta, regsvr32, rundll32, bitsadmin |
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| Recon | sharphound, bloodhound, nmap |
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## Prefetch Directory Location
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```
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C:\Windows\Prefetch\
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```
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Requires admin privileges to read. Enable via:
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```
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reg query "HKLM\SYSTEM\CurrentControlSet\Control\Session Manager\Memory Management\PrefetchParameters"
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```
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## References
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- windowsprefetch PyPI: https://pypi.org/project/windowsprefetch/
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- Windows Prefetch Parser: https://github.com/PoorBillionaire/Windows-Prefetch-Parser
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- libscca/pyscca: https://github.com/libyal/libscca
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- SANS Prefetch Analysis: https://www.sans.org/blog/a-prescription-for-windows-prefetch-analysis
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#!/usr/bin/env python3
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"""Agent for analyzing Windows Prefetch files with Python.
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Parses Prefetch (.pf) files to reconstruct execution history,
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detect renamed/masquerading binaries, and identify suspicious
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tool execution using the windowsprefetch library.
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"""
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import argparse
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import hashlib
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import json
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import os
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from datetime import datetime
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from pathlib import Path
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try:
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import windowsprefetch
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except ImportError:
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windowsprefetch = None
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SUSPICIOUS_EXECUTABLES = {
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"mimikatz", "psexec", "psexesvc", "procdump", "lazagne",
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"rubeus", "sharphound", "bloodhound", "cobalt", "beacon",
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"meterpreter", "powersploit", "empire", "covenant",
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"secretsdump", "wce", "fgdump", "pwdump", "gsecdump",
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"certutil", "bitsadmin", "mshta", "regsvr32", "rundll32",
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"wscript", "cscript", "msiexec", "installutil",
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}
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LOLBINS = {
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"certutil.exe", "bitsadmin.exe", "mshta.exe", "regsvr32.exe",
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"rundll32.exe", "wscript.exe", "cscript.exe", "msiexec.exe",
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"installutil.exe", "regasm.exe", "regsvcs.exe", "msconfig.exe",
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"esentutl.exe", "expand.exe", "extrac32.exe", "findstr.exe",
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"hh.exe", "ie4uinit.exe", "makecab.exe", "replace.exe",
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}
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class PrefetchAnalyzer:
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"""Analyzes Windows Prefetch files for forensic investigation."""
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def __init__(self, output_dir="./prefetch_analysis"):
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.findings = []
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self.executions = []
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def parse_prefetch_file(self, pf_path):
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"""Parse a single Prefetch file and extract execution data."""
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if windowsprefetch is None:
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raise RuntimeError("windowsprefetch not installed: pip install windowsprefetch")
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try:
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pf = windowsprefetch.Prefetch(pf_path)
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except Exception:
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return None
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timestamps = []
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if hasattr(pf, "lastRunTime"):
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timestamps.append(str(pf.lastRunTime))
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if hasattr(pf, "timestamps"):
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timestamps.extend([str(t) for t in pf.timestamps])
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resources = []
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if hasattr(pf, "resources"):
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resources = pf.resources if isinstance(pf.resources, list) else []
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elif hasattr(pf, "filenames"):
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resources = pf.filenames if isinstance(pf.filenames, list) else []
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volumes = []
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if hasattr(pf, "volumes"):
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for v in pf.volumes:
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volumes.append({
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"name": getattr(v, "name", str(v)),
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"serial": getattr(v, "serialNumber", ""),
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})
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entry = {
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"file": str(pf_path),
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"executable": pf.executableName if hasattr(pf, "executableName") else Path(pf_path).stem,
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"run_count": pf.runCount if hasattr(pf, "runCount") else 0,
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"last_run_time": timestamps[0] if timestamps else "",
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"all_timestamps": timestamps,
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"pf_hash": Path(pf_path).stem.split("-")[-1] if "-" in Path(pf_path).stem else "",
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"resources_count": len(resources),
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"volumes": volumes,
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"file_size": os.path.getsize(pf_path),
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"file_sha256": self._hash_file(pf_path),
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}
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self.executions.append(entry)
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return entry
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def _hash_file(self, path):
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h = hashlib.sha256()
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with open(path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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h.update(chunk)
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return h.hexdigest()
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def parse_directory(self, prefetch_dir):
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"""Parse all .pf files in a directory."""
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pf_dir = Path(prefetch_dir)
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pf_files = sorted(pf_dir.glob("*.pf"), key=lambda p: p.stat().st_mtime, reverse=True)
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for pf_file in pf_files:
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self.parse_prefetch_file(str(pf_file))
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return len(pf_files)
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def detect_suspicious(self):
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"""Flag known attack tools and LOLBins."""
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for entry in self.executions:
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exe = entry["executable"].lower()
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exe_base = exe.replace(".exe", "")
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if exe_base in SUSPICIOUS_EXECUTABLES:
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self.findings.append({
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"severity": "critical", "type": "Attack Tool Executed",
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"detail": f"{entry['executable']} run {entry['run_count']} times, "
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f"last: {entry['last_run_time']}",
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})
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elif exe in LOLBINS:
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if entry["run_count"] > 10:
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self.findings.append({
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"severity": "medium", "type": "LOLBin High Usage",
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"detail": f"{entry['executable']} run {entry['run_count']} times",
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})
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def detect_renamed_binaries(self):
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"""Detect potential binary renaming/masquerading."""
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for entry in self.executions:
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exe = entry["executable"].upper()
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pf_name = Path(entry["file"]).stem.upper()
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expected_prefix = exe.replace(".EXE", "")
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if not pf_name.startswith(expected_prefix):
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self.findings.append({
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"severity": "high", "type": "Possible Renamed Binary",
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"detail": f"PF name '{pf_name}' does not match executable '{exe}'",
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})
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def build_timeline(self):
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"""Build chronological execution timeline."""
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timeline = []
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for entry in self.executions:
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for ts in entry["all_timestamps"]:
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if ts:
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timeline.append({
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"timestamp": ts,
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"executable": entry["executable"],
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"run_count": entry["run_count"],
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})
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timeline.sort(key=lambda x: x["timestamp"], reverse=True)
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return timeline[:100]
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def generate_report(self, prefetch_dir):
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count = self.parse_directory(prefetch_dir)
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self.detect_suspicious()
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self.detect_renamed_binaries()
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timeline = self.build_timeline()
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report = {
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"report_date": datetime.utcnow().isoformat(),
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"prefetch_dir": str(prefetch_dir),
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"total_prefetch_files": count,
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"total_unique_executables": len(self.executions),
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"execution_history": self.executions,
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"execution_timeline": timeline[:50],
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"findings": self.findings,
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"total_findings": len(self.findings),
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}
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out = self.output_dir / "prefetch_analysis_report.json"
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with open(out, "w") as f:
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json.dump(report, f, indent=2, default=str)
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print(json.dumps(report, indent=2, default=str))
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return report
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def main():
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parser = argparse.ArgumentParser(
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description="Analyze Windows Prefetch files for execution forensics"
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)
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parser.add_argument("prefetch_dir", help="Path to directory containing .pf files")
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parser.add_argument("--output-dir", default="./prefetch_analysis")
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args = parser.parse_args()
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os.makedirs(args.output_dir, exist_ok=True)
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analyzer = PrefetchAnalyzer(output_dir=args.output_dir)
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analyzer.generate_report(args.prefetch_dir)
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if __name__ == "__main__":
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main()
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