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análisis-de-artefactos-de-powershell-empire

Detecta artefactos del framework de post-explotación PowerShell Empire en el registro de bloque de scripts de Windows (ID de evento 4104) y en el registro de módulos (ID de evento 4103), incluyendo la cadena de lanzador predeterminada, payloads Base64 codificados con WebClient/FromBase64String, invocaciones de módulos conocidos (Invoke-Mimikatz, Invoke-Kerberoast) y patrones de URL de staging. Útil para buscar o confirmar actividad de C2 de Empire en registros de eventos de Windows.

¿Qué es análisis-de-artefactos-de-powershell-empire?

Esta habilidad permite a un analista de seguridad o a un agente de IA detectar y analizar artefactos del framework de post-explotación PowerShell Empire en registros de eventos de Windows. Se enfoca en los registros de bloque de scripts (ID 4104) y registro de módulos (ID 4103), identificando el lanzador predeterminado de Empire, payloads Base64 que contienen `System.Net.WebClient` y `FromBase64String`, módulos conocidos como Invoke-Mimikatz e Invoke-Kerberoast, así como patrones de URL de staging. Es útil durante investigaciones de incidentes, creación de reglas de detección, caza de amenazas y validación de cobertura de monitoreo. Requiere Python 3.9+ y acceso a registros de eventos de Windows o archivos EVTX exportados. Como resultado, genera un informe JSON con IOCs coincidentes, payloads Base64 decodificados, una línea de tiempo de eventos sospechosos, asignaciones a técnicas MITRE ATT&CK y puntuaciones de severidad.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-powershell-empire-artifacts

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Documentación

Análisis de Artefactos de PowerShell Empire

Resumen

PowerShell Empire es un framework de post-explotación que consta de listeners, stagers y agentes. Sus artefactos dejan trazas detectables en los registros de eventos de Windows, particularmente en el registro de bloque de scripts de PowerShell (ID de evento 4104) y el registro de módulos (ID de evento 4103). Esta habilidad analiza registros de eventos en busca de la cadena de lanzador predeterminada de Empire (powershell -noP -sta -w 1 -enc), payloads codificados en Base64 que contienen System.Net.WebClient y FromBase64String, invocaciones de módulos conocidos (Invoke-Mimikatz, Invoke-Kerberoast, Invoke-TokenManipulation) y patrones de URL de staging.

Cuándo Usar

  • Al investigar incidentes de seguridad que requieran analizar artefactos de PowerShell Empire
  • Al construir reglas de detección o consultas de caza de amenazas para este dominio
  • Cuando analistas de SOC necesiten procedimientos estructurados para este tipo de análisis
  • Al validar la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Requisitos Previos

  • Python 3.9+ con acceso al Registro de Eventos de Windows o archivos EVTX exportados
  • Registro de bloque de scripts de PowerShell (ID de evento 4104) habilitado mediante Política de Grupo
  • Registro de módulos (ID de evento 4103) habilitado para una cobertura integral

Patrones Clave de Detección

  1. Lanzador predeterminado — powershell -noP -sta -w 1 -enc seguido de un bloque Base64
  2. Indicadores de stager — System.Net.WebClient, DownloadData, DownloadString, FromBase64String
  3. Firmas de módulos — Invoke-Mimikatz, Invoke-Kerberoast, Invoke-TokenManipulation, Invoke-PSInject, Invoke-DCOM
  4. Cadenas de agente de usuario — agentes de usuario predeterminados de Empire en la configuración del listener HTTP
  5. URLs de staging — /login/process.php, /admin/get.php y patrones de URI predeterminados similares

Salida

Informe JSON con IOCs coincidentes, payloads Base64 decodificados, línea de tiempo de eventos sospechosos, asignaciones a técnicas MITRE ATT&CK y puntuaciones de severidad.

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

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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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