analyzing-malware-persistence-with-autoruns

analyzing-malware-persistence-with-autoruns is a skill for Claude Code, Codex from autohandai/community-skills. It costs 44 tokens per session (900 once invoked), scanned A, original, Apache-2.0.

A guide to using Windows Sysinternals Autoruns to find programs and settings that start automatically. These startup locations can be used by malware to remain active after a computer restarts.

In plain words
What is it for?
Use it during Windows malware investigations to scan auto-start entries, filter signed files, compare a system with a clean baseline, check file reputation, or inspect an offline disk image.
Why use it?
Malware persistence can be spread across registry entries, services, scheduled tasks, drivers, and other Windows locations. Autoruns brings these locations together for scanning, comparison, and investigation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it during Windows malware investigations to scan auto-start entries, filter signed files, compare a system with a clean baseline, check file reputation, or inspect an offline disk image.

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Install with agentmods
npx agentmods add skills/autohandai/community-skills/analyzing-malware-persistence-with-autoruns
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add autohandai/community-skills --skill analyzing-malware-persistence-with-autoruns
Clone the repo
git clone --depth 1 https://github.com/autohandai/community-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-malware-persistence-with-autoruns

README.md
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Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/autohandai/community-skills/analyzing-malware-persistence-with-autoruns"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-malware-persistence-with-autoruns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 900 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.00900
Opus 5 $0.00022 $0.00450
Sonnet 5 $0.00009 $0.00180
Haiku 4.5 $0.00004 $0.00090

Measured 12d ago against content hash 7f9dc2c49116, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-malware-persistence-with-autoruns scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
analyzing-malware-persistence-with-autoruns/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

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

Practical Steps

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}")

Read the full file on GitHub · 93 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 93 lines · 44 tokens per session scan A 7f9dc2c49116

Subscribe to this mod's changes

analyzing-malware-persistence-with-autoruns is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 900 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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