Communitygithub.com

Kubernetes-Audit-Logs analysieren

Analysiert Kubernetes-Audit-Logs auf sicherheitsrelevante Ereignisse wie unbefugte Zugriffe, Privilegieneskalation und Datendiebstahl.

Was ist Kubernetes-Audit-Logs analysieren?

Diese Fähigkeit ermöglicht es, Kubernetes-Audit-Logs (JSON-Lines-Format) zu parsen und sicherheitsrelevante Ereignisse zu erkennen. Dazu gehören unter anderem pods/exec und pods/attach (Shell-Zugriff in Container), Zugriffe auf Secrets, die Erstellung von ClusterRoleBindings (RBAC-Eskalation), die Erstellung privilegierter Pods sowie anonymer oder nicht authentifizierter Zugriff. Sie richtet sich an SOC-Analysten, Sicherheitsforscher und Plattformentwickler, die Bedrohungsjagd betreiben, Erkennungsregeln schreiben oder die Sicherheitsabdeckung validieren möchten. Die Fähigkeit setzt Kenntnisse in Containersicherheit, Python 3.8+ und Zugriff auf eine Testumgebung voraus. Sie ist plattformunabhängig und kann in jedes Analyse-Framework integriert werden, das JSON-Logs verarbeitet. Der mitgelieferte Python-Code dient als Ausgangspunkt für individuelle Erweiterungen.

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

In Ihrer bevorzugten KI fragen

Öffnet einen neuen Chat, in dem dieser Agent-Skill bereits geladen ist.

Dokumentation

Was macht Kubernetes-Audit-Logs analysieren?

Verwendungszweck

  • Bei der Untersuchung von Sicherheitsvorfällen, die eine Analyse von Kubernetes-Audit-Logs erfordern
  • Beim Erstellen von Erkennungsregeln oder Threat-Hunting-Abfragen für diese Domäne
  • Wenn SOC-Analysten strukturierte Verfahren für diesen Analysetyp benötigen
  • Bei der Validierung der Sicherheitsüberwachungsabdeckung für zugehörige Angriffstechniken

Voraussetzungen

  • Vertrautheit mit Container-Sicherheitskonzepten und -Werkzeugen
  • Zugriff auf eine Test- oder Laborumgebung zur sicheren Ausführung
  • Python 3.8+ mit installierten erforderlichen Abhängigkeiten
  • Entsprechende Autorisierung für alle Testaktivitäten

Anleitung

Parsen Sie Kubernetes-Audit-Logdateien (JSON-Lines-Format), um sicherheitsrelevante Ereignisse zu erkennen, darunter unbefugter Zugriff, Privilegieneskalation und Datendiebstahl.

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 von {user}")

Wichtige zu erkennende Ereignisse:

  1. pods/exec und pods/attach (Shell in Container)
  2. Secrets-Zugriff (get/list/watch)
  3. Erstellung von ClusterRoleBindings (RBAC-Eskalation)
  4. Erstellung privilegierter Pods
  5. Anonymer oder system:unauthenticated-Zugriff

Beispiele

# Secrets-Aufzählung erkennen
if verb in ("get", "list") and resource == "secrets":
    print(f"Secret-Zugriff: {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

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,

Verwandte Skills