跳到正文
cccskills
免费在 GitHub 公开

detecting-lateral-movement-with-splunk

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(8)

  • SKILL.md4.3 KB
  • assets/template.md1.2 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.5 KB
  • references/standards.md2.6 KB
  • references/workflows.md4.6 KB
  • scripts/agent.py4.9 KB
  • scripts/process.py12.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Detecting Lateral Movement with Splunk

When to Use

  • When hunting for adversary movement between compromised systems
  • After detecting credential theft to trace subsequent lateral activity
  • When investigating unusual authentication patterns across the network
  • During incident response to scope the breadth of compromise
  • When proactively hunting for TA0008 (Lateral Movement) techniques

Prerequisites

  • Splunk Enterprise or Splunk Cloud with Windows event data ingested
  • Windows Security Event Logs forwarded (4624, 4625, 4648, 4672, 4768, 4769)
  • Sysmon deployed for process creation and network connection data
  • Network flow data or firewall logs for SMB/RDP/WinRM correlation
  • Active Directory user and group membership reference data

Workflow

  1. Define Lateral Movement Scope: Identify which lateral movement techniques to hunt (RDP, SMB/Admin Shares, WinRM, PsExec, WMI, DCOM, SSH).
  2. Query Authentication Events: Use SPL to search for Type 3 (Network) and Type 10 (RemoteInteractive) logons across the environment.
  3. Build Authentication Graphs: Map source-to-destination authentication relationships to identify unusual connection patterns.
  4. Detect First-Time Relationships: Identify new source-destination pairs that have not been seen in the historical baseline.
  5. Correlate with Process Activity: Link authentication events to subsequent process creation on destination hosts.
  6. Identify Anomalous Patterns: Flag lateral movement to sensitive servers, unusual hours, service account misuse, or rapid multi-host access.
  7. Report and Contain: Document lateral movement path, affected systems, and coordinate containment response.

Key Concepts

ConceptDescription
T1021Remote Services (parent technique)
T1021.001Remote Desktop Protocol (RDP)
T1021.002SMB/Windows Admin Shares
T1021.003Distributed COM (DCOM)
T1021.004SSH
T1021.006Windows Remote Management (WinRM)
T1570Lateral Tool Transfer
T1047Windows Management Instrumentation
T1569.002Service Execution (PsExec)
Logon Type 3Network logon (SMB, WinRM, mapped drives)
Logon Type 10Remote Interactive (RDP)
Event ID 4624Successful logon
Event ID 4648Explicit credential logon (runas, PsExec)

Tools & Systems

ToolPurpose
Splunk EnterpriseSIEM for log aggregation and SPL queries
Splunk Enterprise SecurityThreat detection and notable events
Windows Event ForwardingCentralize Windows logs
SysmonDetailed process and network telemetry
BloodHoundAD attack path analysis
PingCastleAD security assessment

Common Scenarios

  1. PsExec Lateral Movement: Adversary uses PsExec to execute commands on remote systems via SMB, generating Type 3 logon with ADMIN$ share access.
  2. RDP Pivoting: Attacker RDPs to internal systems using stolen credentials, creating Type 10 logon events.
  3. WMI Remote Execution: Adversary uses WMIC process call create to spawn processes on remote hosts.
  4. WinRM PowerShell Remoting: Attacker uses Enter-PSSession or Invoke-Command to execute code on remote systems.
  5. Pass-the-Hash via SMB: Compromised NTLM hashes used to authenticate to remote systems without knowing the plaintext password.

Output Format

Hunt ID: TH-LATMOV-[DATE]-[SEQ]
Movement Type: [RDP/SMB/WinRM/WMI/DCOM/PsExec]
Source Host: [Hostname/IP]
Destination Host: [Hostname/IP]
Account Used: [Username]
Logon Type: [3/10/other]
First Seen: [Timestamp]
Event Count: [Number of events]
Risk Level: [Critical/High/Medium/Low]
Lateral Movement Path: [A -> B -> C -> D]

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.

中文简介正在准备中。当前显示原文说明。

aniket2348823/Vul-Agent22026年6月9日 更新

查看 aniket2348823 的全部技能

报告此技能的问题