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Análisis de malware Android con Apktool

Realiza análisis estático de malware en APK Android usando apktool para descompilación de recursos, jadx para recuperación de código Java y androguard para inspección de manifiesto, detección de combinaciones peligrosas de permisos e identificación de código ofuscado, carga dinámica de código y llamadas a API basadas en reflexión. Útil para hacer triaje estático de un APK sospechoso sin ejecutarlo o para construir reglas de detección de malware móvil.

¿Qué es Análisis de malware Android con Apktool?

Esta habilidad se centra en el análisis estático de archivos APK de Android sospechosos de ser malware, utilizando herramientas como apktool, jadx y androguard, sin necesidad de ejecutar la muestra. Permite extraer permisos, actividades, servicios, receptores de broadcast y llamadas a API sospechosas de forma programática. Es especialmente útil para analistas de SOC que necesitan triar rápidamente un APK, identificar combinaciones peligrosas de permisos, patrones de código ofuscado, carga dinámica de código y llamadas a API basadas en reflexión, así como indicadores de red (URLs, IPs, C2). El resultado final es un informe JSON con análisis de permisos, listado de componentes, APIs sospechosas, indicadores de red y una puntuación de riesgo mapeada a MITRE ATT&CK móvil. Soporta las plataformas de agentes de IA como Claude Code, Cursor y Codex.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-android-malware-with-apktool
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Documentación

¿Qué hace Análisis de malware Android con Apktool?

Resumen

El malware Android distribuido como archivos APK se puede analizar estáticamente para extraer permisos, actividades, servicios, receptores de broadcast y llamadas a API sospechosas sin ejecutar la muestra. Esta habilidad utiliza androguard para el análisis programático de APK, identificando combinaciones peligrosas de permisos, patrones de código ofuscado, carga dinámica de código, llamadas a API basadas en reflexión e indicadores de comunicación de red.

Cuándo usarlo

  • Cuando investigas incidentes de seguridad que requieren analizar malware Android con apktool
  • Cuando construyes reglas de detección o consultas de caza de amenazas para este dominio
  • Cuando los analistas de SOC necesitan procedimientos estructurados para este tipo de análisis
  • Cuando validas la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Prerrequisitos

  • Python 3.9+ con androguard
  • apktool (para descompilación de recursos)
  • jadx (para recuperación de código Java, opcional)
  • Entorno de análisis aislado (VM o sandbox)
  • Archivos APK de muestra para análisis

Pasos

  1. Analizar APK con androguard para extraer metadatos del manifiesto
  2. Enumerar los permisos solicitados y marcar combinaciones peligrosas
  3. Listar actividades, servicios, receptores y proveedores del manifiesto
  4. Escanear en busca de llamadas a API sospechosas (reflexión, criptografía, SMS, telefonía)
  5. Detectar patrones de carga dinámica de código (DexClassLoader, Runtime.exec)
  6. Extraer URLs, IPs e indicadores C2 incrustados de las cadenas
  7. Generar un informe de evaluación de riesgos con mapeos MITRE ATT&CK móvil

Salida esperada

  • Informe JSON con análisis de permisos, listado de componentes, llamadas a API sospechosas, indicadores de red y puntuación de riesgo
  • Cadenas extraídas y posibles IOCs del APK

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-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,

analyzing-docker-container-forensics

Investigate compromised Docker containers by analyzing images, layers,

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