analyzing-malware-persistence-with-autoruns

Use Sysinternals Autoruns to systematically identify and analyze malware persistence mechanisms across registry keys, scheduled tasks, services, drivers, and startup locations on Windows systems.

Quality

60%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

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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

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

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}")

Validation Criteria

All ASEP categories scanned and cataloged
Unsigned entries flagged for investigation
Suspicious paths and LOLBin launch strings highlighted
Baseline comparison identifies new persistence mechanisms

References

Repository: mukul975/Anthropic-Cybersecurity-Skills
Commit: c15f73d

Last updated: 1 day ago
Created: 1 day ago

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