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Analyse von PowerShell-Empire-Artefakten

Erkennen Sie Artefakte des Post-Exploitation-Frameworks PowerShell Empire in der Windows-Script-Block-Protokollierung (Ereignis-ID 4104) und der Modulprotokollierung (Ereignis-ID 4103), einschließlich des Standard-Launcher-Strings, Base64-codierter WebClient/FromBase64String-Payloads, bekannter Modulaufrufe (Invoke-Mimikatz, Invoke-Kerberoast) sowie Staging-URL-Mustern. Einsetzbar bei der Jagd nach oder Bestätigung von Empire-C2-Aktivitäten in Windows-Ereignisprotokollen.

Was ist Analyse von PowerShell-Empire-Artefakten?

Diese Fähigkeit unterstützt Sicherheitsanalysten und Threat Hunter bei der Erkennung von PowerShell-Empire-Artefakten in Windows-Ereignisprotokollen. Sie analysiert speziell PowerShell Script Block Logging (Ereignis-ID 4104) und Module Logging (Ereignis-ID 4103) auf bekannte Indikatoren des Empire-Frameworks, wie den Standard-Launcher-String (`powershell -noP -sta -w 1 -enc`), Base64-codierte Payloads mit `System.Net.WebClient` und `FromBase64String`, sowie Modulsignaturen wie Invoke-Mimikatz, Invoke-Kerberoast und Invoke-TokenManipulation. Die Fähigkeit ist nützlich, um Sicherheitsvorfälle zu untersuchen, Erkennungsregeln zu erstellen oder die Abdeckung der Sicherheitsüberwachung für Post-Exploitation-Techniken zu validieren. Sie generiert einen strukturierten JSON-Report mit gefundenen IOCs, decodierten Base64-Payloads, einer Timeline verdächtiger Ereignisse, MITRE-ATT&CK-Technikzuordnungen und Schweregradbewertungen. Voraussetzung ist Python 3.9+ sowie Zugriff auf Windows-Ereignisprotokolle oder exportierte EVTX-Dateien, wobei PowerShell Script Block Logging und Module Logging aktiviert sein sollten.

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Dokumentation

Was macht Analyse von PowerShell-Empire-Artefakten?

Überblick

PowerShell Empire ist ein Post-Exploitation-Framework, das aus Listenern, Stagern und Agents besteht. Seine Artefakte hinterlassen erkennbare Spuren in Windows-Ereignisprotokollen, insbesondere in der PowerShell-Script-Block-Protokollierung (Ereignis-ID 4104) und der Modulprotokollierung (Ereignis-ID 4103). Mit dieser Fähigkeit werden Ereignisprotokolle auf Empires Standard-Launcher-String (powershell -noP -sta -w 1 -enc), Base64-codierte Payloads mit System.Net.WebClient und FromBase64String, bekannte Modulaufrufe (Invoke-Mimikatz, Invoke-Kerberoast, Invoke-TokenManipulation) und Staging-URL-Muster analysiert.

Verwendung

  • Bei der Untersuchung von Sicherheitsvorfällen, die die Analyse von PowerShell-Empire-Artefakten erfordern
  • Beim Erstellen von Erkennungsregeln oder Threat-Hunting-Abfragen für diesen Bereich
  • Wenn SOC-Analysten strukturierte Verfahren für diese Analyseart benötigen
  • Bei der Validierung der Sicherheitsüberwachungsabdeckung für verwandte Angriffstechniken

Voraussetzungen

  • Python 3.9+ mit Zugriff auf das Windows-Ereignisprotokoll oder exportierte EVTX-Dateien
  • PowerShell Script Block Logging (Ereignis-ID 4104), aktiviert über Gruppenrichtlinien
  • Modulprotokollierung (Ereignis-ID 4103), aktiviert für eine umfassende Abdeckung

Wichtige Erkennungsmuster

  1. Standard-Launcher – powershell -noP -sta -w 1 -enc gefolgt von einem Base64-Block
  2. Stager-Indikatoren – System.Net.WebClient, DownloadData, DownloadString, FromBase64String
  3. Modulsignaturen – Invoke-Mimikatz, Invoke-Kerberoast, Invoke-TokenManipulation, Invoke-PSInject, Invoke-DCOM
  4. User-Agent-Strings – Standard-Empire-User-Agents in der HTTP-Listener-Konfiguration
  5. Staging-URLs – /login/process.php, /admin/get.php und ähnliche Standard-URI-Muster

Ausgabe

JSON-Report mit gefundenen IOCs, decodierten Base64-Payloads, einer Timeline verdächtiger Ereignisse, MITRE-ATT&CK-Technikzuordnungen und Schweregradbewertungen.

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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