analyzing-malware-persistence-with-autoruns

name	analyzing-malware-persistence-with-autoruns
description	Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems. Use when working with analyzing malware persistence with autoruns.
domain	cybersecurity
subdomain	malware-analysis
tags	["autoruns","persistence","malware-analysis","sysinternals","windows","registry","startup","incident-response"]
mitre_attack	["T1547","T1053","T1543","T1546"]
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Executable Denylisting","Execution Isolation","File Metadata Consistency Validation","Content Format Conversion","File Content Analysis"]
nist_csf	["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"]

Analyzing Malware Persistence with Autoruns

Overview

Sysinternals Autoruns extracts data from hundreds of Auto-Start Extensibility Points (ASEPs) on Windows, scanning 18+ categories including Run/RunOnce keys, services, scheduled tasks, drivers, Winlogon entries, LSA providers, print monitors, WMI subscriptions, and AppInit DLLs. Digital signature verification filters Microsoft-signed entries. The compare function identifies newly added persistence via baseline diffing. VirusTotal integration checks hash reputation. Offline analysis via -z flag enables forensic disk image examination.

When to Use

Trigger phrases:

• "analyzing malware persistence with autoruns"

• "Use Sysinternals Autoruns to systematically identify and analyze malware persist"

• When investigating security incidents that require analyzing malware persistence with autoruns

• 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

• Sysinternals Autoruns (GUI) and Autorunsc (CLI)
• Administrative privileges on target system
• Python 3.9+ for automated analysis
• VirusTotal API key for reputation checks
• Clean baseline export for comparison

Workflow

1. Isolate the sample — ensure the malware is in a sandboxed environment with no network access
2. Record file metadata — hash the sample and note file type, size, and compile timestamp
3. Static analysis — examine strings, imports, and disassembled code without execution
4. Dynamic analysis — execute in a monitored sandbox and record behavior (file, registry, network)
5. Document IOCs — extract indicators of compromise and write the analysis report

Step 1: Automated Persistence Scanning

#!/usr/bin/env python3
"""Automate Autoruns-based persistence analysis."""
import subprocess
import csv
import json
import sys


def scan_and_analyze(autorunsc_path="autorunsc64.exe", csv_path="scan.csv"):
    cmd = [autorunsc_path, "-a", "*", "-c", "-h", "-s", "-nobanner", "*"]
    result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
    with open(csv_path, 'w') as f:
        f.write(result.stdout)
    return parse_and_flag(csv_path)


def parse_and_flag(csv_path):
    suspicious = []
    with open(csv_path, 'r', errors='replace') as f:
        for row in csv.DictReader(f):
            reasons = []
            signer = row.get("Signer", "")
            if not signer or signer == "(Not verified)":
                reasons.append("Unsigned binary")
            if not row.get("Description") and not row.get("Company"):
                reasons.append("Missing metadata")
            path = row.get("Image Path", "").lower()
            for sp in ["\temp\\", "\appdata\local\temp", "\users\public\\"]:
                if sp in path:
                    reasons.append(f"Suspicious path")
            launch = row.get("Launch String", "").lower()
            for kw in ["powershell", "cmd /c", "wscript", "mshta", "regsvr32"]:
                if kw in launch:
                    reasons.append(f"LOLBin: {kw}")
            if reasons:
                row["reasons"] = reasons
                suspicious.append(row)
    return suspicious


if __name__ == "__main__":
    if len(sys.argv) > 1:
        results = parse_and_flag(sys.argv[1])
        print(f"[!] {len(results)} suspicious entries")
        for r in results:
            print(f"  {r.get('Entry','')} - {r.get('Image Path','')}")
            for reason in r.get('reasons', []):
                print(f"    - {reason}")