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analyzing-malware-persistence-with-autoruns

Use Sysinternals Autoruns to systematically enumerate and analyze malware

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Category

Security

Supported Platforms

Universal

Our assessment of analyzing-malware-persistence-with-autoruns

analyzing-malware-persistence-with-autoruns scores 88/100 on our quality scale, 289th of 544 Security skills we index.

Its SKILL.md is 4.6 KB long, well organised into 8 sections with 1 code example: a solid amount of guidance for an agent.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so analyzing-malware-persistence-with-autoruns is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

analyzing-malware-persistence-with-autoruns compared with similar skills

All 4 of these similar skills score higher than analyzing-malware-persistence-with-autoruns; compare them before choosing.

SkillScoreStarsUpdatedFormat
analyzing-malware-persistence-with-autoruns (this skill)by mukul9758833.3k25d agoSKILL.md
algorithmic-artby anthropics100177.9k3d agoSKILL.md
pptxby anthropics100177.9k3d agoSKILL.md
designby nextlevelbuilder100130.2k4d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k4d agoSKILL.md

Frequently asked questions

How do I install analyzing-malware-persistence-with-autoruns?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-malware-persistence-with-autoruns. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-malware-persistence-with-autoruns work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-malware-persistence-with-autoruns safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-malware-persistence-with-autoruns still maintained?
The repository was last updated 25 days ago, so analyzing-malware-persistence-with-autoruns is actively maintained.

name: analyzing-malware-persistence-with-autoruns description: Use Sysinternals Autoruns to systematically enumerate and analyze malware persistence mechanisms across Windows registry run keys, scheduled tasks, services, drivers, and startup locations. Use when hunting for persistence during Windows incident response, triaging a compromised endpoint, or validating that malware autostart entries have been fully identified and removed. domain: cybersecurity subdomain: malware-analysis tags:

  • autoruns
  • persistence
  • malware-analysis
  • sysinternals
  • windows
  • registry
  • startup
  • incident-response mitre_attack:
  • T1547.001
  • T1543.003
  • T1053.005
  • T1574.001
  • T1037.001 version: '1.0' author: mahipal 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

  • 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

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions