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analisando-persistencia-de-malware-com-autoruns

Use o Sysinternals Autoruns para enumerar e analisar sistematicamente mecanismos de persistência de malware.

O que é analisando-persistencia-de-malware-com-autoruns?

Esta skill capacita agentes de IA (como Claude Code, Cursor, Codex, etc.) a executar análises forenses de persistência de malware no Windows usando o Sysinternals Autoruns, tanto na interface gráfica quanto na CLI. O Autoruns examina mais de 18 categorias de ASEPs, incluindo chaves Run/RunOnce, serviços, tarefas agendadas, drivers, entradas Winlogon, provedores LSA, monitores de impressão, assinaturas WMI e DLLs AppInit. A skill automatiza a varredura com um script Python que gera um CSV detalhado, identifica entradas suspeitas (não assinadas, metadados ausentes, caminhos suspeitos como \temp\, \users\public\, e strings de inicialização com LOLBins como PowerShell, cmd /c, wscript, etc.) e realiza verificação de reputação via integração com VirusTotal. Suporta análise offline de imagens de disco forense com o parâmetro -z, e comparação de linhas de base para detectar novos mecanismos de persistência. É ideal para SOCs, caça a ameaças, validação de cobertura de monitoramento e construção de regras de detecção alinhadas ao MITRE ATT&CK T1547.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-malware-persistence-with-autoruns

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Documentação

Analisando Persistência de Malware com Autoruns

Visão Geral

O Sysinternals Autoruns extrai dados de centenas de Pontos de Extensibilidade de Inicialização Automática (ASEPs) no Windows, escaneando mais de 18 categorias, incluindo chaves Run/RunOnce, serviços, tarefas agendadas, drivers, entradas Winlogon, provedores LSA, monitores de impressão, assinaturas WMI e DLLs AppInit. A verificação de assinatura digital filtra entradas assinadas pela Microsoft. A função de comparação identifica persistência recém-adicionada via diff de linha de base. A integração com VirusTotal verifica a reputação de hashes. A análise offline através da flag -z permite examinar imagens de disco forense.

Quando Usar

  • Ao investigar incidentes de segurança que exigem análise de persistência de malware com autoruns
  • Ao construir regras de detecção ou consultas de caça a ameaças para este domínio
  • Quando analistas de SOC precisam de procedimentos estruturados para este tipo de análise
  • Ao validar a cobertura de monitoramento de segurança para técnicas de ataque relacionadas

Pré-requisitos

  • Sysinternals Autoruns (GUI) e Autorunsc (CLI)
  • Privilégios administrativos no sistema alvo
  • Python 3.9+ para análise automatizada
  • Chave de API VirusTotal para verificações de reputação
  • Exportação de linha de base limpa para comparação

Fluxo de Trabalho

Passo 1: Varredura Automatizada de Persistência

#!/usr/bin/env python3
"""Automatiza análise de persistência baseada em Autoruns."""
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("Binário não assinado")
            if not row.get("Description") and not row.get("Company"):
                reasons.append("Metadados ausentes")
            path = row.get("Image Path", "").lower()
            for sp in ["\\temp\\", "\\appdata\\local\\temp", "\\users\\public\\"]:
                if sp in path:
                    reasons.append(f"Caminho suspeito")
            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)} entradas suspeitas")
        for r in results:
            print(f"  {r.get('Entry','')} - {r.get('Image Path','')}")
            for reason in r.get('reasons', []):
                print(f"    - {reason}")

Critérios de Validação

  • Todas as categorias ASEP escaneadas e catalogadas
  • Entradas não assinadas sinalizadas para investigação
  • Caminhos suspeitos e strings de inicialização LOLBin destacados
  • Comparação de linha de base identifica novos mecanismos de persistência

Referências

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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