---
name: analyzing-prefetch-files-for-execution-history
description: Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an investigation.
domain: cybersecurity
subdomain: digital-forensics
tags:
- forensics
- prefetch
- windows-artifacts
- execution-history
- timeline-analysis
- evidence-collection
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- RS.AN-03
- DE.AE-02
- RS.MA-01
mitre_attack:
- T1059.001
- T1003.001
- T1021.002
- T1567.002
---

# Analyzing Prefetch Files for Execution History

## When to Use
- When determining which programs were executed on a Windows system and when
- During malware investigations to confirm execution of suspicious binaries
- For establishing a timeline of application usage during an incident
- When correlating program execution with other forensic artifacts
- To identify anti-forensic tools or unauthorized software that was run

## Prerequisites
- Access to Windows Prefetch directory (C:\Windows\Prefetch\) from forensic image
- PECmd (Eric Zimmerman), WinPrefetchView, or python-prefetch parser
- Understanding of Prefetch file format (versions 17, 23, 26, 30)
- Windows system with Prefetch enabled (default on client OS, disabled on servers)
- Knowledge of Prefetch naming conventions (APPNAME-HASH.pf)

## Workflow

### Step 1: Extract Prefetch Files from Forensic Image

```bash
# Mount the forensic image
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

# Copy all prefetch files
mkdir -p /cases/case-2024-001/prefetch/
cp /mnt/evidence/Windows/Prefetch/*.pf /cases/case-2024-001/prefetch/

# Count and list prefetch files
ls -la /cases/case-2024-001/prefetch/ | wc -l
ls -la /cases/case-2024-001/prefetch/ | head -30

# Hash all prefetch files for integrity
sha256sum /cases/case-2024-001/prefetch/*.pf > /cases/case-2024-001/prefetch/pf_hashes.txt

# Note: Prefetch filename format is EXECUTABLE_NAME-XXXXXXXX.pf
# The hash (XXXXXXXX) is based on the executable path
# Same executable from different paths creates different prefetch files
```

### Step 2: Parse Prefetch Files with PECmd

```bash
# Using Eric Zimmerman's PECmd (Windows or via Mono/Wine on Linux)
# Download from https://ericzimmerman.github.io/

# Parse a single prefetch file
PECmd.exe -f "C:\cases\prefetch\POWERSHELL.EXE-A]B2C3D4.pf"

# Parse all prefetch files and output to CSV
PECmd.exe -d "C:\cases\prefetch\" --csv "C:\cases\analysis\" --csvf prefetch_results.csv

# Parse with JSON output
PECmd.exe -d "C:\cases\prefetch\" --json "C:\cases\analysis\" --jsonf prefetch_results.json

# Output includes for each file:
# - Executable name and path
# - Run count
# - Last run time (up to 8 timestamps in Windows 10)
# - Files and directories referenced during execution
# - Volume information (serial number, creation date)
# - Prefetch file creation time
```

### Step 3: Parse with Python for Linux-Based Analysis

```bash
pip install prefetch

python3 << 'PYEOF'
import os
import json
from datetime import datetime

# Parse prefetch files using python
import struct

def parse_prefetch(filepath):
    """Parse a Windows Prefetch file."""
    with open(filepath, 'rb') as f:
        data = f.read()

    # Check for MAM compressed format (Windows 10)
    if data[:4] == b'MAM\x04':
        import lznt1  # or use DecompressBuffer
        # Windows 10 prefetch files are compressed
        print(f"  [Compressed Win10 format - use PECmd for full parsing]")
        return None

    # Version 17 (XP), 23 (Vista/7), 26 (8.1), 30 (10)
    version = struct.unpack('<I', data[0:4])[0]
    signature = data[4:8]

    if signature != b'SCCA':
        print(f"  Invalid prefetch signature")
        return None

    file_size = struct.unpack('<I', data[8:12])[0]
    exec_name = data[16:76].decode('utf-16-le').strip('\x00')
    run_count = struct.unpack('<I', data[208:212])[0] if version >= 23 else struct.unpack('<I', data[144:148])[0]

    result = {
        'version': version,
        'executable': exec_name,
        'file_size': file_size,
        'run_count': run_count,
    }

    # Extract last execution timestamps
    if version == 23:  # Vista/7 - 1 timestamp
        ts = struct.unpack('<Q', data[128:136])[0]
        result['last_run'] = filetime_to_datetime(ts)
    elif version >= 26:  # Win8+ - up to 8 timestamps
        timestamps = []
        for i in range(8):
            ts = struct.unpack('<Q', data[128+i*8:136+i*8])[0]
            if ts > 0:
                timestamps.append(filetime_to_datetime(ts))
        result['last_run_times'] = timestamps

    return result

def filetime_to_datetime(ft):
    """Convert Windows FILETIME to datetime string."""
    if ft == 0:
        return None
    timestamp = (ft - 116444736000000000) / 10000000
    try:
        return datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')
    except (OSError, ValueError):
        return None

# Process all prefetch files
prefetch_dir = '/cases/case-2024-001/prefetch/'
results = []

for filename in sorted(os.listdir(prefetch_dir)):
    if filename.lower().endswith('.pf'):
        filepath = os.path.join(prefetch_dir, filename)
        print(f"\n=== {filename} ===")
        result = parse_prefetch(filepath)
        if result:
            print(f"  Executable: {result['executable']}")
            print(f"  Run Count:  {result['run_count']}")
            if 'last_run' in result:
                print(f"  Last Run:   {result['last_run']}")
            elif 'last_run_times' in result:
                for i, ts in enumerate(result['last_run_times']):
                    print(f"  Run Time {i+1}: {ts}")
            results.append(result)

# Save results
with open('/cases/case-2024-001/analysis/prefetch_analysis.json', 'w') as f:
    json.dump(results, f, indent=2)
PYEOF
```

### Step 4: Identify Suspicious Execution Evidence

```bash
# Search for known malicious tool names in prefetch
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(MIMIKATZ|PSEXEC|WMIC|COBALT|BEACON|PWDUMP|PROCDUMP|LAZAGNE|RUBEUS|BLOODHOUND|SHARPHOUND|CERTUTIL|BITSADMIN)'

# Search for script interpreters (potential malicious execution)
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(POWERSHELL|CMD\.EXE|WSCRIPT|CSCRIPT|MSHTA|REGSVR32|RUNDLL32|MSIEXEC)'

# Search for remote access tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(TEAMVIEWER|ANYDESK|LOGMEIN|VNC|SPLASHTOP|SCREENCONNECT|AMMYY)'

# Search for data exfiltration tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(RAR|7Z|ZIP|RCLONE|MEGA|DROPBOX|ONEDRIVE|GDRIVE|FTP|CURL|WGET)'

# Find recently created prefetch files (newest executables run)
ls -lt /cases/case-2024-001/prefetch/ | head -20

# Cross-reference with Shimcache and Amcache for confirmation
# Prefetch existence = program was executed at least once
```

### Step 5: Build Execution Timeline

```bash
# Create timeline from prefetch data
python3 << 'PYEOF'
import json
import csv

with open('/cases/case-2024-001/analysis/prefetch_analysis.json') as f:
    data = json.load(f)

timeline = []
for entry in data:
    if 'last_run_times' in entry:
        for ts in entry['last_run_times']:
            if ts:
                timeline.append({
                    'timestamp': ts,
                    'executable': entry['executable'],
                    'run_count': entry['run_count'],
                    'source': 'Prefetch'
                })
    elif 'last_run' in entry and entry['last_run']:
        timeline.append({
            'timestamp': entry['last_run'],
            'executable': entry['executable'],
            'run_count': entry['run_count'],
            'source': 'Prefetch'
        })

# Sort chronologically
timeline.sort(key=lambda x: x['timestamp'])

# Write timeline CSV
with open('/cases/case-2024-001/analysis/execution_timeline.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['timestamp', 'executable', 'run_count', 'source'])
    writer.writeheader()
    writer.writerows(timeline)

# Print suspicious time window
for entry in timeline:
    if '2024-01-15' in entry['timestamp'] or '2024-01-16' in entry['timestamp']:
        print(f"  {entry['timestamp']} | {entry['executable']} (x{entry['run_count']})")
PYEOF
```

## Key Concepts

| Concept | Description |
|---------|-------------|
| Prefetch | Windows performance optimization that pre-loads application data and tracks execution |
| SCCA signature | Magic bytes identifying a valid Prefetch file |
| Path hash | CRC-based hash of the executable path forming part of the .pf filename |
| Run count | Number of times the executable has been launched (may wrap around) |
| Last run timestamps | Windows 8+ stores up to 8 most recent execution timestamps |
| Referenced files | List of files and directories accessed during the first 10 seconds of execution |
| Volume information | Drive serial number and creation date identifying the source volume |
| MAM compression | Windows 10 Prefetch files use MAM4 compression requiring decompression before parsing |

## Tools & Systems

| Tool | Purpose |
|------|---------|
| PECmd | Eric Zimmerman's Prefetch parser with CSV/JSON output |
| WinPrefetchView | NirSoft GUI tool for viewing Prefetch files |
| python-prefetch | Python library for parsing Prefetch files |
| Prefetch Hash Calculator | Tool to calculate expected hash from executable paths |
| KAPE | Automated artifact collection including Prefetch |
| Autopsy | Forensic platform with Prefetch analysis module |
| Plaso/log2timeline | Super-timeline tool that includes Prefetch parser |
| Velociraptor | Endpoint agent with Prefetch collection and analysis artifacts |

## Common Scenarios

**Scenario 1: Confirming Malware Execution**
Search Prefetch directory for the malware executable name, confirm execution via Prefetch existence, extract run count and last run time, identify referenced DLLs to understand malware behavior, correlate with registry autorun entries.

**Scenario 2: Attacker Tool Usage Timeline**
Identify Prefetch files for PsExec, Mimikatz, BloodHound, and other attacker tools, build chronological timeline of tool execution, determine the sequence of the attack (reconnaissance, credential theft, lateral movement), match timestamps with network connection logs.

**Scenario 3: Data Staging and Exfiltration**
Look for Prefetch entries of compression tools (7z, WinRAR, zip), identify execution of file transfer utilities (rclone, FTP clients), check for cloud storage client execution, timeline when data staging and transfer occurred.

**Scenario 4: Anti-Forensics Detection**
Check for execution of known anti-forensic tools (CCleaner, Eraser, SDelete), identify if Prefetch directory was recently cleared (fewer files than expected for active system), note timestamps of anti-forensic tool execution relative to other evidence.

## Output Format

```
Prefetch Analysis Summary:
  System: Windows 10 Pro (Build 19041)
  Prefetch Files: 234
  Analysis Period: All available execution history

  Execution Statistics:
    Total unique executables: 234
    First execution: 2023-06-15 (system install)
    Latest execution: 2024-01-18 23:45 UTC

  Suspicious Executions:
    MIMIKATZ.EXE-5F2A3B1C.pf
      Run Count: 3 | Last: 2024-01-16 02:30:15 UTC
    PSEXEC.EXE-AD70946C.pf
      Run Count: 7 | Last: 2024-01-16 02:45:30 UTC
    RCLONE.EXE-1F3E5A2B.pf
      Run Count: 2 | Last: 2024-01-17 03:15:00 UTC
    POWERSHELL.EXE-022A1004.pf
      Run Count: 145 | Last: 2024-01-18 14:00:00 UTC

  Attack Timeline (from Prefetch):
    2024-01-15 14:32 - POWERSHELL.EXE (initial access)
    2024-01-16 02:30 - MIMIKATZ.EXE (credential theft)
    2024-01-16 02:45 - PSEXEC.EXE (lateral movement)
    2024-01-17 03:15 - RCLONE.EXE (data exfiltration)

  Report: /cases/case-2024-001/analysis/execution_timeline.csv
```
