performing-lateral-movement-detection▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI execution, RDP pivoting, and SMB-based spreading using SIEM correlation of Windows event logs, network flow data, and endpoint telemetry mapped to MITRE ATT&CK Lateral Movement (TA0008) techniques.
| name | performing-lateral-movement-detection |
| description | 'Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI execution, RDP pivoting, and SMB-based spreading using SIEM correlation of Windows event logs, network flow data, and endpoint telemetry mapped to MITRE ATT&CK Lateral Movement (TA0008) techniques. ' |
| domain | cybersecurity |
| subdomain | soc-operations |
| tags | - soc - lateral-movement - mitre-attack - pass-the-hash - psexec - wmi - rdp - smb - detection |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| d3fend_techniques | - Token Binding - Execution Isolation - Restore Access - Application Protocol Command Analysis - Process Termination |
| nist_csf | - DE.CM-01 - DE.AE-02 - RS.MA-01 - DE.AE-06 |
Performing Lateral Movement Detection
When to Use
Use this skill when:
- SOC teams need to detect attackers pivoting between systems after initial compromise
- Incident investigations require tracking an attacker's movement path through the network
- Detection engineering needs lateral movement rules mapped to ATT&CK TA0008 techniques
- Red/purple team exercises identify lateral movement detection gaps
Do not use for detecting initial access or external attacks — lateral movement detection focuses on internal host-to-host pivot activity.
Prerequisites
- Windows Security Event Logs (EventCode 4624, 4625, 4648, 4672) from all endpoints and servers
- Sysmon deployed with process creation (EventCode 1), network connections (EventCode 3), and named pipe (EventCode 17/18)
- Network flow data (NetFlow/sFlow, Zeek connection logs) for internal traffic analysis
- SIEM with cross-source correlation capability
- Baseline of normal internal authentication patterns
Workflow
Step 1: Detect Pass-the-Hash / Pass-the-Ticket (T1550)
Pass-the-Hash Detection (EventCode 4624 with NTLM):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=4624 Logon_Type=3
AuthenticationPackageName="NTLM"
| where TargetUserName!="ANONYMOUS LOGON" AND TargetUserName!="$"
| stats count, dc(ComputerName) AS unique_targets, values(ComputerName) AS targets
by src_ip, TargetUserName
| where unique_targets > 3
| eval alert = "Possible Pass-the-Hash: NTLM network logon to ".unique_targets." hosts"
| sort - unique_targets
| table src_ip, TargetUserName, unique_targets, count, targets, alert
Overpass-the-Hash Detection (Kerberos with RC4):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=4769
TicketEncryptionType="0x17"
| where ServiceName!="krbtgt" AND ServiceName!="$"
| stats count, dc(ServiceName) AS unique_services by src_ip, TargetUserName
| where count > 5
| eval alert = "Possible Overpass-the-Hash: RC4 Kerberos tickets from ".src_ip
| table _time, src_ip, TargetUserName, unique_services, count, alert
Golden/Silver Ticket Detection (T1558):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=4769
| where TicketOptions="0x40810000" OR TicketOptions="0x40800000"
| eval ticket_lifetime = TicketExpireTime - TicketIssueTime
| where ticket_lifetime > 36000 --- >10 hours (abnormal)
| stats count by src_ip, TargetUserName, ServiceName, TicketEncryptionType, TicketOptions
| eval alert = "Possible Golden/Silver Ticket: Abnormal ticket properties"
Step 2: Detect Remote Service Exploitation (T1021)
PsExec Detection (T1021.002):
--- Via Sysmon process creation
index=sysmon EventCode=1
(Image="*\\psexec.exe" OR Image="*\\psexesvc.exe"
OR OriginalFileName="psexec.c" OR OriginalFileName="psexesvc.exe"
OR ParentImage="*\\psexesvc.exe")
| table _time, Computer, User, ParentImage, Image, CommandLine, Hashes
--- Via named pipe creation (Sysmon EventCode 17)
index=sysmon EventCode=17
PipeName IN ("\\PSEXESVC*", "\\RemCom*", "\\csexec*")
| table _time, Computer, User, Image, PipeName
--- Via Windows service creation (EventCode 7045)
index=wineventlog sourcetype="WinEventLog:System" EventCode=7045
ServiceName="PSEXESVC" OR ServiceFileName="*PSEXESVC*"
| table _time, Computer, ServiceName, ServiceFileName, AccountName
WMI Remote Execution (T1047):
index=sysmon EventCode=1
(Image="*\\wmic.exe" AND CommandLine="*/node:*")
OR (ParentImage="*\\WmiPrvSE.exe" AND Image IN ("*\\cmd.exe", "*\\powershell.exe"))
| eval execution_type = case(
match(Image, "wmic"), "WMI Command Line",
match(ParentImage, "WmiPrvSE"), "WMI Provider Host (remote execution)"
)
| table _time, Computer, User, execution_type, ParentImage, Image, CommandLine
WinRM/PowerShell Remoting (T1021.006):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=4624
Logon_Type=3 AuthenticationPackageName="Kerberos"
| where ProcessName="*\\wsmprovhost.exe" OR ProcessName="*\\powershell.exe"
| stats count, dc(ComputerName) AS unique_targets by src_ip, TargetUserName
| where unique_targets > 2
| eval alert = "PowerShell Remoting to ".unique_targets." hosts from ".src_ip
--- Sysmon variant
index=sysmon EventCode=1
ParentImage="*\\wsmprovhost.exe"
Image IN ("*\\cmd.exe", "*\\powershell.exe", "*\\csc.exe")
| table _time, Computer, User, Image, CommandLine
RDP Lateral Movement (T1021.001):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=4624 Logon_Type=10
| stats count, dc(ComputerName) AS rdp_targets, values(ComputerName) AS destinations,
earliest(_time) AS first_rdp, latest(_time) AS last_rdp
by src_ip, TargetUserName
| where rdp_targets > 2
| eval duration_hours = round((last_rdp - first_rdp) / 3600, 1)
| eval alert = TargetUserName." RDP'd to ".rdp_targets." hosts in ".duration_hours." hours"
| sort - rdp_targets
Step 3: Detect SMB-Based Lateral Movement
Anomalous SMB Traffic Patterns:
index=firewall OR index=zeek sourcetype IN ("pan:traffic", "bro:conn:json")
dest_port=445 action=allowed
| where src_ip!=dest_ip
| stats count AS smb_sessions, dc(dest_ip) AS unique_targets,
sum(bytes_out) AS total_bytes
by src_ip
| where unique_targets > 10
| eval alert = case(
unique_targets > 50, "CRITICAL: Mass SMB enumeration from ".src_ip,
unique_targets > 20, "HIGH: Significant SMB lateral movement",
unique_targets > 10, "MEDIUM: Elevated SMB connections"
)
| sort - unique_targets
Admin Share Access (C$, ADMIN$):
index=wineventlog sourcetype="WinEventLog:Security" EventCode=5140
ShareName IN ("\\\\*\\C$", "\\\\*\\ADMIN$", "\\\\*\\IPC$")
| where SubjectUserName!="SYSTEM" AND SubjectUserName!="$"
| stats count, dc(ComputerName) AS unique_hosts by SubjectUserName, ShareName, src_ip
| where unique_hosts > 3
| eval alert = "Admin share access to ".unique_hosts." hosts by ".SubjectUserName
| sort - unique_hosts
Step 4: Build Lateral Movement Graph
Visualize the attack path:
--- Build source->destination graph for authentication events
index=wineventlog EventCode=4624 Logon_Type IN (3, 10)
earliest=-24h
| stats count AS connections, latest(_time) AS last_connection
by src_ip, ComputerName, TargetUserName, Logon_Type
| eval edge = src_ip." -> ".ComputerName." (User: ".TargetUserName.", Type: ".Logon_Type.")"
| sort - connections
| table edge, connections, last_connection
--- Network flow correlation
index=netflow earliest=-24h
dest_port IN (445, 135, 3389, 5985, 5986)
| stats sum(bytes) AS total_bytes, count AS flow_count,
dc(dest_ip) AS targets by src_ip, dest_port
| where targets > 5
| eval service = case(
dest_port=445, "SMB",
dest_port=135, "RPC/WMI",
dest_port=3389, "RDP",
dest_port IN (5985, 5986), "WinRM"
)
| sort - targets
| table src_ip, service, targets, flow_count, total_bytes
Step 5: Detect DCOM and Scheduled Task-Based Movement
DCOM Lateral Execution (T1021.003):
index=sysmon EventCode=1
ParentImage IN ("*\\mmc.exe", "*\\excel.exe", "*\\outlook.exe")
Image IN ("*\\cmd.exe", "*\\powershell.exe", "*\\mshta.exe")
| where ParentCommandLine="*-Embedding*"
| eval alert = "DCOM-based lateral movement: ".ParentImage." spawned ".Image
| table _time, Computer, User, ParentImage, Image, CommandLine, alert
Remote Scheduled Task Creation (T1053.005):
index=wineventlog EventCode=4698
| where SubjectUserName!="SYSTEM"
| eval task_xml = TaskContent
| search task_xml="*http*" OR task_xml="*powershell*" OR task_xml="*cmd*" OR task_xml="*\\Temp\\*"
| table _time, Computer, SubjectUserName, TaskName, task_xml
Step 6: Correlate Movement with Kill Chain Phases
Build end-to-end attack chain detection:
--- Detect complete lateral movement sequence
index=wineventlog OR index=sysmon
(EventCode=4625 OR EventCode=4624 OR EventCode=1 OR EventCode=4698 OR EventCode=5140)
| eval phase = case(
EventCode=4625, "1-Recon/BruteForce",
EventCode=4624 AND Logon_Type=3, "2-Lateral Movement",
EventCode=5140 AND match(ShareName, "C\$|ADMIN\$"), "3-Admin Share Access",
EventCode=1 AND match(ParentImage, "psexesvc|WmiPrvSE|wsmprovhost"), "4-Remote Execution",
EventCode=4698, "5-Persistence (Scheduled Task)",
1=1, "other"
)
| where phase!="other"
| stats count by phase, src_ip, ComputerName, TargetUserName
| sort phase, _time
| table phase, src_ip, ComputerName, TargetUserName, count
Key Concepts
| Term | Definition |
|---|---|
| Lateral Movement | Post-compromise technique where attackers pivot between systems to reach targets |
| Pass-the-Hash | Using stolen NTLM hash for authentication without knowing the plaintext password |
| Pass-the-Ticket | Using stolen Kerberos TGT/TGS tickets for authentication across the domain |
| PsExec | Sysinternals tool (and attack technique) for remote process execution via SMB and named pipes |
| WMI Execution | Using Windows Management Instrumentation for remote command execution via DCOM or WinRM |
| Admin Share | Default Windows administrative shares (C$, ADMIN$, IPC$) used for remote system management |
Tools & Systems
- Splunk Enterprise Security: SIEM platform for correlating Windows events, Sysmon, and network flows
- Microsoft Defender for Identity: Cloud service detecting lateral movement via domain controller monitoring
- BloodHound: Active Directory attack path analysis tool for identifying lateral movement opportunities
- CrowdStrike Falcon: EDR platform with lateral movement detection and automated containment
- Zeek (Bro): Network monitor generating connection logs for SMB, RDP, and WinRM traffic analysis
Common Scenarios
- PsExec Spread: Attacker uses PsExec to execute malware across 20 workstations — detect via service creation events
- RDP Pivoting: Compromised VPN account used to RDP through multiple internal hosts — detect via Logon_Type 10 chains
- WMI Recon and Execution: Attacker uses WMI for discovery then execution — detect via WmiPrvSE child processes
- Pass-the-Hash Campaign: Stolen local admin hash used across subnet — detect via NTLM Logon_Type 3 to multiple hosts
- Scheduled Task Persistence: Remote scheduled task created on domain controller — detect via EventCode 4698 from non-admin source
Output Format
LATERAL MOVEMENT DETECTION REPORT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Period: 2024-03-15 14:00 to 18:00 UTC
Source: 192.168.1.105 (WORKSTATION-042)
Movement Path:
14:23 192.168.1.105 → 10.0.5.20 (DC-PRIMARY) — PtH via NTLM Type 3
14:25 10.0.5.20 → 10.0.5.21 (DC-BACKUP) — Kerberos ticket reuse
14:28 10.0.5.20 → 10.0.10.15 (FILESERVER-01) — PsExec service creation
14:32 10.0.10.15 → 10.0.10.20 (DB-PRIMARY) — WMI remote execution
14:35 10.0.10.20 → 10.0.10.25 (DB-BACKUP) — SMB admin share access
Techniques Detected:
T1550.002 — Pass-the-Hash (NTLM authentication to DC)
T1021.002 — PsExec (remote service installation)
T1047 — WMI Execution (WmiPrvSE child process)
T1021.002 — SMB Admin Share (C$ access on DB-BACKUP)
Affected Systems: 5 hosts across 2 network segments
User Account: admin_compromised (Domain Admin)
Containment: All 5 hosts isolated at 14:45 UTC
How to use performing-lateral-movement-detection on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-lateral-movement-detection
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-lateral-movement-detection from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-lateral-movement-detection. Access the skill through slash commands (e.g., /performing-lateral-movement-detection) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★55 reviews- ★★★★★Valentina Yang· Dec 20, 2024
performing-lateral-movement-detection reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Layla Chen· Dec 12, 2024
Solid pick for teams standardizing on skills: performing-lateral-movement-detection is focused, and the summary matches what you get after install.
- ★★★★★Mei White· Dec 12, 2024
We added performing-lateral-movement-detection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chaitanya Patil· Dec 4, 2024
We added performing-lateral-movement-detection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★James Choi· Dec 4, 2024
performing-lateral-movement-detection has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Piyush G· Nov 23, 2024
performing-lateral-movement-detection fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Emma Lopez· Nov 23, 2024
Useful defaults in performing-lateral-movement-detection — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hassan Thomas· Nov 3, 2024
I recommend performing-lateral-movement-detection for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Valentina Martin· Nov 3, 2024
Registry listing for performing-lateral-movement-detection matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ishan Jain· Nov 3, 2024
performing-lateral-movement-detection fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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