「lateral-movement」の検索結果

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Active Directory, Kerberos, LDAP, OAuth, enterprise messaging, Windows host forensics, credential material, and lateral-movement challenges. Use when the user asks to trace tickets or tokens, inspect mailbox rules, analyze Windows host evidence, understand an AD trust path, or explain a lateral-movement chain across sandbox-linked nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

日本語の概要は準備中です。原文の説明を表示しています。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

日本語の概要は準備中です。原文の説明を表示しています。

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service (WMI/PsExec/RDP) abuse. Use when hunting for MITRE ATT&CK TA0008 lateral movement activity or investigating suspected pivoting between hosts during an incident, with Splunk as the SIEM.

日本語の概要は準備中です。原文の説明を表示しています。

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks. Use when monitoring historians bridging IT/OT zones for compromise, investigating historian-specific CVE exploitation, or validating historian data integrity after a suspected OT incident.

日本語の概要は準備中です。原文の説明を表示しています。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Active Directory, Kerberos, LDAP, OAuth, enterprise messaging, Windows host forensics, credential material, and lateral-movement challenges. Use when the user asks to trace tickets or tokens, inspect mailbox rules, analyze Windows host evidence, understand an AD trust path, or explain a lateral-movement chain across sandbox-linked nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

日本語の概要は準備中です。原文の説明を表示しています。

Execute commands and binaries on remote Windows systems utilizing PsExec and SMB/RPC mechanisms. This skill details the mechanics behind tools like Sysinternals PsExec, Impacket's psexec.py, and their role in lateral movement via hidden administrative shares.

日本語の概要は準備中です。原文の説明を表示しています。

Execute lateral movement within Microsoft Entra ID (formerly Azure AD) and Microsoft 365 environments. Use this skill to traverse cloud resources using compromised user tokens, managed identities, Primary Refresh Tokens (PRT), and application permissions (Service Principals) to achieve Global Administrator access.

日本語の概要は準備中です。原文の説明を表示しています。

Hunts for lateral movement via remote service creation, admin share writes, and remote WMI process creation by correlating Windows logon, service install, and process-creation events. Activates for requests to hunt lateral movement, detect PsExec/WMIexec activity, or find remote execution over SMB and WMI.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

日本語の概要は準備中です。原文の説明を表示しています。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

tespit etme
lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, Pass-the-Hash activity, remote service execution, and anomalous internal connections.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

Run CloudFox's read-only Describe/List/Get enumeration (all-checks, role-trusts, secrets, endpoints, and permissions commands) to map AWS and Azure attack paths and surface exploitable misconfigurations. Use immediately after obtaining a cloud credential to build situational awareness, find exposed resources and secrets, or map sts:AssumeRole trust relationships for lateral-movement/privilege-escalation planning.

日本語の概要は準備中です。原文の説明を表示しています。