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análisis-de-registros-de-auditoría-de-kubernetes

Analiza registros de auditoría de Kubernetes para detectar eventos de seguridad como accesos no autorizados, escalada de privilegios y exfiltración de datos.

¿Qué es análisis-de-registros-de-auditoría-de-kubernetes?

Esta habilidad permite a asistentes de IA (como Claude Code, Cursor, Codex) analizar archivos de registro de auditoría de Kubernetes en formato JSON lines para identificar eventos de seguridad. Es útil para investigadores de seguridad, ingenieros de SOC y desarrolladores que necesitan construir reglas de detección, realizar búsquedas de amenazas o validar la cobertura de monitoreo frente a técnicas de ataque específicas. La habilidad se apoya en scripts de Python que recorren el archivo de registro, parsean cada evento y filtran por verbos y recursos sensibles (pods/exec, secrets, clusterrolebindings, pods privilegiados, accesos anónimos). Está orientada a entornos con Python 3.8+ y acceso a archivos de registro de auditoría de Kubernetes en el sistema de archivos local.

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

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Documentación

Análisis de Registros de Auditoría de Kubernetes

Cuándo Usarlo

  • Al investigar incidentes de seguridad que requieran analizar registros de auditoría de Kubernetes
  • Al construir reglas de detección o consultas de búsqueda de amenazas para este dominio
  • Cuando los analistas de SOC necesiten 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 seguridad de contenedores
  • 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

Analiza archivos de registro de auditoría de Kubernetes (formato JSON lines) para detectar eventos relevantes para la seguridad, incluyendo accesos no autorizados, escalada de privilegios y exfiltración de datos.

import json

with open("/var/log/kubernetes/audit.log") as f:
    for line in f:
        event = json.loads(line)
        verb = event.get("verb")
        resource = event.get("objectRef", {}).get("resource")
        user = event.get("user", {}).get("username")
        if verb == "create" and resource == "pods/exec":
            print(f"Pod exec por {user}")

Eventos clave a detectar:

  1. pods/exec y pods/attach (shell dentro de contenedores)
  2. Acceso a secrets (get/list/watch)
  3. Creación de clusterrolebindings (escalada RBAC)
  4. Creación de pods privilegiados
  5. Acceso anónimo o system:unauthenticated

Ejemplos

# Detectar enumeración de secrets
if verb in ("get", "list") and resource == "secrets":
    print(f"Acceso a secreto: {user} -> {event['objectRef'].get('name')}")

Individual skills in this repo

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

abusing-dpapi-for-credential-access

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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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