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Análisis del Panorama de Amenazas con MISP

Consulta una instancia de MISP (Plataforma de Intercambio de Información sobre Malware) a través de PyMISP.

¿Qué es Análisis del Panorama de Amenazas con MISP?

Esta habilidad permite a un agente de IA (como Claude Code, Cursor, etc.) conectarse a una instancia de MISP (Malware Information Sharing Platform) y extraer un análisis estructurado del panorama de amenazas. Es útil para analistas de SOC, investigadores de seguridad y desarrolladores de reglas de detección que necesitan comprender rápidamente las tendencias de amenazas, los actores, las técnicas MITRE ATT&CK y los indicadores de compromiso (IOC). Soporta cualquier entorno donde se pueda ejecutar Python con PyMISP. El agente configura la conexión, extrae estadísticas de eventos, distribuciones de tipos de atributos, técnicas de ATT&CK, actividad de actores de amenazas (galaxy clusters) y genera análisis de tendencias temporales de IOC, produciendo un informe JSON consolidado.

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

¿Qué hace Análisis del Panorama de Amenazas con MISP?

Cuándo usarlo

  • Al investigar incidentes de seguridad que requieren analizar el panorama de amenazas con MISP
  • Al construir reglas de detección o consultas de búsqueda de amenazas para este dominio
  • Cuando los analistas del SOC necesitan procedimientos estructurados para este tipo de análisis
  • Al validar la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Prerrequisitos

  • Familiaridad con conceptos y herramientas de inteligencia de amenazas
  • Acceso a un entorno de prueba o laboratorio para ejecución segura
  • Python 3.8+ con las dependencias requeridas instaladas
  • Autorización apropiada para cualquier actividad de prueba

Instrucciones

  1. Instalar dependencias: pip install pymisp
  2. Configurar la URL de MISP y la clave API.
  3. Ejecutar el agente para generar el análisis del panorama de amenazas:
    • Obtener estadísticas de eventos por nivel de amenaza y rango de fechas
    • Analizar distribuciones de tipos de atributos (IP, dominio, hash, URL)
    • Identificar las principales técnicas MITRE ATT&CK a partir de las etiquetas de eventos
    • Rastrear la actividad de actores de amenazas mediante clústeres de galaxias
    • Generar análisis de tendencias temporales de envíos de IOC
python scripts/agent.py --misp-url https://misp.local --api-key TU_CLAVE --days 90 --output landscape_report.json

Ejemplos

Resumen del Panorama de Amenazas

Periodo: Últimos 90 días
Eventos analizados: 1,247
Nivel de amenaza principal: Alto (43%)
Tipo de atributo principal: ip-dst (31%), dominio (22%), sha256 (18%)
Técnica MITRE principal: T1566 Phishing (89 eventos)
Actor de amenaza principal: APT28 (34 eventos)

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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