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分析 PowerShell Empire 后利用框架痕迹

检测 Windows 脚本块日志 (事件 ID 4104) 和模块日志 (事件 ID 4103) 中的 PowerShell Empire 后利用框架痕迹,包括默认启动器字符串、Base64 编码的 WebClient/FromBase64String 载荷、已知模块调用 (Invoke-Mimikatz, Invoke-Kerberoast) 以及暂存 URL 模式。用于在 Windows 事件日志中搜寻或确认 Empire C2 活动时使用。

分析 PowerShell Empire 后利用框架痕迹 是什么?

该技能专门用于检测 PowerShell Empire 后利用框架在 Windows 事件日志中留下的痕迹。它通过分析脚本块日志 (Event ID 4104) 和模块日志 (Event ID 4103) 来识别关键的检测模式,例如默认的启动器字符串、包含 `System.Net.WebClient` 和 `FromBase64String` 的 Base64 编码载荷、已知的模块调用(如 Invoke-Mimikatz、Invoke-Kerberoast)以及特定的暂存 URL 模式。该技能对于安全事件调查、威胁狩猎、检测规则开发以及验证安全监控覆盖范围非常有用。它要求 Python 3.9+ 环境,并需要启用相应的 Windows 日志策略。输出为结构化的 JSON 报告,包含匹配的 IOC、解码后的载荷、可疑事件时间线、MITRE ATT&CK 技术映射以及严重性评分,帮助分析人员快速确认 Empire C2 活动并进行深入取证。

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分析 PowerShell Empire 后利用框架痕迹 是做什么的?

概述

PowerShell Empire 是一个后利用框架,由监听器、存根和代理组成。其痕迹会在 Windows 事件日志中留下可检测的特征,特别是 PowerShell 脚本块日志 (事件 ID 4104) 和模块日志 (事件 ID 4103)。该技能分析事件日志中 Empire 的默认启动器字符串 (powershell -noP -sta -w 1 -enc)、包含 System.Net.WebClient 和 FromBase64String 的 Base64 编码载荷、已知的模块调用 (Invoke-Mimikatz, Invoke-Kerberoast, Invoke-TokenManipulation) 以及暂存 URL 模式。

使用时机

  • 在调查需要分析 PowerShell Empire 后利用框架痕迹的安全事件时
  • 在为此领域构建检测规则或威胁狩猎查询时
  • 当 SOC 分析师需要针对此分析类型的结构化流程时
  • 在验证相关攻击技术的安全监控覆盖率时

先决条件

  • Python 3.9+,能够访问 Windows 事件日志或导出的 EVTX 文件
  • 通过组策略启用的 PowerShell 脚本块日志 (事件 ID 4104)
  • 启用模块日志 (事件 ID 4103) 以获得全面覆盖

关键检测模式

  1. 默认启动器 — powershell -noP -sta -w 1 -enc 后跟 Base64 数据块
  2. 存根指示器 — System.Net.WebClient、DownloadData、DownloadString、FromBase64String
  3. 模块签名 — Invoke-Mimikatz、Invoke-Kerberoast、Invoke-TokenManipulation、Invoke-PSInject、Invoke-DCOM
  4. 用户代理字符串 — HTTP 监听器配置中的默认 Empire 用户代理
  5. 暂存 URL — /login/process.php、/admin/get.php 等默认 URI 模式

输出

包含匹配的 IOC、解码的 Base64 载荷、可疑事件时间线、MITRE ATT&CK 技术映射以及严重性评分的 JSON 报告。

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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