detecting-lateral-movement-in-network

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-lateral-movement-in-network
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summary

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.

skill.md
name
detecting-lateral-movement-in-network
description
'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. '
domain
cybersecurity
subdomain
network-security
tags
- network-security - lateral-movement - threat-detection - siem - pass-the-hash
version
'1.0'
author
mahipal
license
Apache-2.0
d3fend_techniques
- Application Protocol Command Analysis - Network Isolation - Network Traffic Analysis - Client-server Payload Profiling - Network Traffic Community Deviation
nist_csf
- PR.IR-01 - DE.CM-01 - ID.AM-03 - PR.DS-02

Detecting Lateral Movement in Network

When to Use

  • Monitoring enterprise networks for post-compromise lateral movement patterns (pass-the-hash, RDP hopping, PSExec)
  • Building SIEM detection rules and alerts for common MITRE ATT&CK lateral movement techniques (T1021, T1570)
  • Investigating suspected breaches by analyzing authentication patterns and network connections between internal hosts
  • Hunting for anomalous east-west traffic patterns that indicate an attacker pivoting through the network
  • Validating that network segmentation and access controls effectively limit lateral movement paths

Do not use as a substitute for endpoint detection and response (EDR) tools, for monitoring only north-south traffic while ignoring internal traffic flows, or without baseline knowledge of normal internal communication patterns.

Prerequisites

  • Network security monitoring deployed at internal choke points (Zeek, Suricata, or network TAPs)
  • SIEM platform (Splunk, Elastic, Microsoft Sentinel) collecting Windows Security Event Logs, DNS, and flow data
  • Windows Event Log forwarding configured for Security events (4624, 4625, 4648, 4672, 4768, 4769)
  • Baseline of normal internal authentication and connection patterns
  • Understanding of MITRE ATT&CK Lateral Movement tactics (TA0008)

Workflow

Step 1: Configure Log Collection for Lateral Movement Detection

# Windows Event Logs to collect (via WEF or agent):
# Security Log:
#   4624 - Successful logon (Type 3=Network, Type 10=RemoteInteractive)
#   4625 - Failed logon
#   4648 - Logon using explicit credentials (RunAs, PsExec)
#   4672 - Special privileges assigned (admin logon)
#   4768 - Kerberos TGT request
#   4769 - Kerberos service ticket request
#   4776 - NTLM authentication (credential validation)
# System Log:
#   7045 - New service installed (PsExec indicator)
#   7036 - Service started/stopped

# Configure Windows Event Forwarding (WEF) subscription
# On the collector server (PowerShell):
# wecutil cs lateral-movement-subscription.xml

# Filebeat configuration for Windows Event Log shipping
cat > /etc/filebeat/modules.d/security.yml << 'EOF'
- module: system
  auth:
    enabled: true
    var.paths: ["/var/log/auth.log"]
  syslog:
    enabled: true

- module: zeek
  connection:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/conn.log"]
  dns:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/dns.log"]
  smb_mapping:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/smb_mapping.log"]
  dce_rpc:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/dce_rpc.log"]
EOF

# Zeek configuration for lateral movement detection
# Enable SMB, DCE-RPC, and Kerberos logging
cat >> /opt/zeek/share/zeek/site/local.zeek << 'EOF'
@load policy/protocols/smb
@load policy/protocols/conn/known-hosts
@load policy/protocols/conn/known-services
@load frameworks/intel/seen
EOF

sudo zeekctl deploy

Step 2: Build Detection Rules for Common Lateral Movement Techniques

# Splunk SPL queries for lateral movement detection

# 1. Detect PsExec usage (new service creation on remote hosts)
# index=wineventlog EventCode=7045 ServiceName="PSEXESVC" OR ServiceName="*psexec*"
# | stats count by ComputerName, ServiceName, ImagePath
# | where count > 0

# 2. Detect Pass-the-Hash (Type 3 logon with NTLM)
# index=wineventlog EventCode=4624 LogonType=3 AuthenticationPackageName="NTLM"
# | where TargetUserName!="ANONYMOUS LOGON" AND TargetUserName!="$"
# | stats count dc(ComputerName) as unique_hosts by TargetUserName, IpAddress
# | where unique_hosts > 3

# 3. Detect RDP lateral movement (Type 10 logon from internal IPs)
# index=wineventlog EventCode=4624 LogonType=10
# | where cidrmatch("10.0.0.0/8", IpAddress) OR cidrmatch("192.168.0.0/16", IpAddress)
# | stats count dc(ComputerName) as rdp_hosts by TargetUserName, IpAddress
# | where rdp_hosts > 2

# Elastic SIEM detection rules (KQL)
# event.code: "4624" and winlog.event_data.LogonType: "3"
#   and winlog.event_data.AuthenticationPackageName: "NTLM"
#   and not winlog.event_data.TargetUserName: *$
#   and source.ip: (10.0.0.0/8 or 172.16.0.0/12 or 192.168.0.0/16)
# Sigma rules for lateral movement detection
# Install sigma and convert to target SIEM format
pip3 install sigma-cli

cat > lateral_movement_pth.yml << 'EOF'
title: Pass-the-Hash Lateral Movement Detection
id: f8d98d6c-7a07-4d74-b064-dd4a3c244528
status: experimental
description: Detects network logon with NTLM authentication to multiple hosts
logsource:
    product: windows
    service: security
detection:
    selection:
        EventID: 4624
        LogonType: 3
        AuthenticationPackageName: NTLM
    filter:
        TargetUserName|endswith: '$'
    condition: selection and not filter
    timeframe: 15m
    count:
        field: ComputerName
        min: 3
        group-by: TargetUserName
level: high
tags:
    - attack.lateral_movement
    - attack.t1550.002
EOF

# Convert Sigma rule to Splunk SPL
sigma convert -t splunk lateral_movement_pth.yml

# Convert to Elastic query
sigma convert -t elasticsearch lateral_movement_pth.yml

Step 3: Network-Level Detection with Zeek

# Detect SMB lateral movement (admin$ and c$ share access)
cat /opt/zeek/logs/current/smb_mapping.log | \
  zeek-cut ts id.orig_h id.resp_h path | \
  grep -iE "(admin\$|c\$|ipc\$)" | \
  sort -t$'\t' -k2 | uniq -c | sort -rn

# Detect hosts connecting to many internal hosts on port 445 (SMB spreading)
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p | \
  awk '$4 == 445' | \
  awk '{print $2}' | sort | uniq -c | sort -rn | head -10

# Detect WMI lateral movement (DCE-RPC to IWbemServices)
cat /opt/zeek/logs/current/dce_rpc.log | \
  zeek-cut ts id.orig_h id.resp_h operation | \
  grep -i "wbem\|wmi" | sort | uniq -c | sort -rn

# Detect RDP connections between internal hosts
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p duration | \
  awk '$4 == 3389 && $5 > 60' | \
  sort -t$'\t' -k2 | head -20

# Detect Kerberos ticket-granting anomalies
cat /opt/zeek/logs/current/kerberos.log | \
  zeek-cut ts id.orig_h id.resp_h client service success error_msg | \
  grep -v "true" | head -20

# Custom Zeek script for lateral movement detection
sudo tee /opt/zeek/share/zeek/site/custom-detections/lateral-movement.zeek << 'ZEEKEOF'
@load base/frameworks/notice
@load base/frameworks/sumstats

module LateralMovement;

export {
    redef enum Notice::Type += {
        SMB_Lateral_Spread,
        RDP_Lateral_Chain
    };
    const smb_host_threshold: count = 5 &redef;
    const smb_time_window: interval = 15min &redef;
}

event zeek_init()
{
    local r1 = SumStats::Reducer(
        $stream="lateral.smb",
        $apply=set(SumStats::UNIQUE)
    );

    SumStats::create([
        $name="detect-smb-lateral",
        $epoch=smb_time_window,
        $reducers=set(r1),
        $threshold_val(key: SumStats::Key, result: SumStats::Result) = {
            return result["lateral.smb"]$unique + 0.0;
        },
        $threshold=smb_host_threshold + 0.0,
        $threshold_crossed(key: SumStats::Key, result: SumStats::Result) = {
            NOTICE([
                $note=SMB_Lateral_Spread,
                $msg=fmt("Host %s connected to %d SMB hosts in %s",
                         key$str, result["lateral.smb"]$unique, smb_time_window),
                $identifier=key$str
            ]);
        }
    ]);
}

event connection_state_remove(c: connection)
{
    if ( c$id$resp_p == 445/tcp && c$id$resp_h in Site::local_nets )
    {
        SumStats::observe("lateral.smb",
            [$str=cat(c$id$orig_h)],
            [$str=cat(c$id$resp_h)]
        );
    }
}
ZEEKEOF

sudo zeekctl deploy

Step 4: Threat Hunting for Lateral Movement Indicators

# Hunt for authentication anomalies in Windows logs
# Splunk query: Users authenticating from unusual source hosts
# index=wineventlog EventCode=4624 LogonType=3
# | stats values(IpAddress) as source_ips dc(IpAddress) as source_count by TargetUserName
# | where source_count > 5
# | sort -source_count

# Hunt for service accounts used interactively
# index=wineventlog EventCode=4624 (LogonType=2 OR LogonType=10)
# | where match(TargetUserName, "^svc-.*")
# | table _time ComputerName TargetUserName IpAddress LogonType

# Network flow analysis for lateral movement patterns
# Look for hosts that suddenly start communicating with many internal hosts
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h | \
  awk '{
    key = $2
    targets[key][$3] = 1
  }
  END {
    for (src in targets) {
      count = 0
      for (dst in targets[src]) count++
      if (count > 20) print src, count
    }
  }' | sort -k2 -rn

# Detect credential dumping artifacts (large LSASS reads)
# Look for connections from hosts that suddenly pivot
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p orig_bytes | \
  awk '$4 == 445 && $5 > 10000000' | sort -t$'\t' -k5 -rn

# Timeline analysis: map the attack path
# index=wineventlog (EventCode=4624 OR EventCode=7045)
# | eval stage=case(
#     EventCode=4624 AND LogonType=3, "Network Logon",
#     EventCode=4624 AND LogonType=10, "RDP Logon",
#     EventCode=7045, "Service Creation"
#   )
# | timechart span=5m count by stage

Step 5: Automated Response and Containment

# SOAR playbook for lateral movement response (pseudocode)
# When lateral movement alert triggers:

# 1. Enrich the alert with context
# - Query AD for user group membership and role
# - Check if source IP is a known admin workstation
# - Look up recent vulnerability scan results for affected hosts

# 2. Automated containment actions
# Option A: Isolate the host via switch port shutdown
# ssh admin@switch "conf t; interface Gi1/0/5; shutdown"

# Option B: Quarantine via VLAN change (less disruptive)
# ssh admin@switch "conf t; interface Gi1/0/5; switchport access vlan 999"

# Option C: Block at firewall
sudo iptables -I FORWARD -s 10.10.5.23 -j DROP

# 3. Disable the compromised account
# PowerShell: Disable-ADAccount -Identity compromised_user

# 4. Force password reset
# PowerShell: Set-ADAccountPassword -Identity compromised_user -Reset

# 5. Collect forensic evidence before full containment
# velociraptor artifact collect Windows.KapeFiles.Targets --target BasicCollection

Step 6: Build Detection Dashboard

# Elastic Kibana dashboard queries for lateral movement monitoring

# Panel 1: Authentication heatmap (source vs destination)
# Aggregation: Terms on source.ip (rows) and destination.ip (columns)
# Metric: Count of event.code:4624

# Panel 2: SMB connections between internal hosts
# Filter: destination.port:445 and source.ip:10.0.0.0/8
# Aggregation: Top 20 source IPs by unique destination count

# Panel 3: RDP sessions timeline
# Filter: destination.port:3389 and event.code:4624 and winlog.event_data.LogonType:10
# Visualization: Timeline by source.ip

# Panel 4: New service installations
# Filter: event.code:7045
# Aggregation: Terms on winlog.event_data.ServiceName

# Panel 5: Failed authentication spike detection
# Filter: event.code:4625
# Aggregation: Date histogram with anomaly detection

# Export Kibana dashboard
# curl -X GET "elastic-siem:5601/api/saved_objects/_export" \
#   -H "kbn-xsrf: true" \
#   -d '{"type":"dashboard","objects":[{"id":"lateral-movement-dashboard","type":"dashboard"}]}' \
#   > lateral_movement_dashboard.ndjson

Key Concepts

TermDefinition
Lateral MovementMITRE ATT&CK tactic (TA0008) describing techniques attackers use to move through a network from one compromised system to another
Pass-the-Hash (T1550.002)Using captured NTLM password hashes to authenticate to remote systems without knowing the plaintext password
PsExec (T1569.002)Remote service execution tool that creates a temporary service on the target system, detectable by Event ID 7045
East-West TrafficNetwork communication between internal systems (as opposed to north-south traffic between internal and external networks)
Authentication AnomalyDeviation from baseline authentication patterns such as a user logging into systems they never accessed before
Kerberoasting (T1558.003)Requesting Kerberos service tickets for service accounts and cracking them offline, detectable via Event ID 4769 anomalies

Tools & Systems

  • Zeek: Network security monitor generating SMB, Kerberos, DCE-RPC, and connection logs for lateral movement analysis
  • Splunk/Elastic SIEM: Log aggregation platforms for correlating authentication events, network flows, and service creation across the enterprise
  • Sigma: Vendor-agnostic detection rule format for writing portable lateral movement detection rules across SIEM platforms
  • Velociraptor: Endpoint forensics tool for collecting evidence from hosts involved in lateral movement chains
  • BloodHound: Active Directory attack path analysis tool for identifying potential lateral movement routes before attackers exploit them

Common Scenarios

Scenario: Detecting a Ransomware Operator's Lateral Movement

Context: The SOC receives an alert for PsExec service creation on a file server (10.10.20.15) at 2:00 AM. The alert triggers a lateral movement investigation. The organization has Zeek network monitoring and Windows Event Log forwarding to Splunk.

Approach:

  1. Query Splunk for Event ID 7045 (service creation) on 10.10.20.15 to confirm PsExec execution and identify the source IP (10.10.5.23)
  2. Trace authentication history for 10.10.5.23: find Event ID 4624 Type 3 logons, discovering the host authenticated to 8 servers in the past hour using NTLM (pass-the-hash pattern)
  3. Check Zeek conn.log for 10.10.5.23: identify SMB connections (port 445) to 12 internal hosts and large file transfers to an external IP
  4. Build the attack timeline: initial compromise via phishing at 1:15 AM, credential dumping at 1:25 AM, lateral movement to 8 servers between 1:30-2:00 AM
  5. Identify all compromised hosts by tracing authentication chains: 10.10.5.23 -> 10.10.20.15 -> 10.10.20.16 -> 10.10.20.17
  6. Contain by quarantining all identified hosts to VLAN 999, disabling the compromised account, and blocking the external C2 IP
  7. Report the complete attack chain with timeline, affected hosts, and detection gaps

Pitfalls:

  • Only investigating the single alert instead of tracing the full lateral movement chain across all hosts
  • Not checking for persistence mechanisms on each compromised host before declaring containment
  • Relying solely on Windows Event Logs without correlating network flow data, missing lateral movement via tools that do not generate Windows events
  • Not establishing a baseline of normal internal authentication patterns, making anomaly detection impossible

Output Format

## Lateral Movement Investigation Report

**Case ID**: IR-2024-0312
**Initial Alert**: PsExec on 10.10.20.15 at 02:00 UTC
**Investigation Period**: 2024-03-15 01:00 to 03:00 UTC

### Attack Timeline

| Time (UTC) | Source | Destination | Technique | Evidence |
|------------|--------|-------------|-----------|----------|
| 01:15 | External | 10.10.5.23 | Initial Access (Phishing) | Email log + HTTP download |
| 01:25 | 10.10.5.23 | Local | Credential Dumping | LSASS access (Sysmon EID 10) |
| 01:32 | 10.10.5.23 | 10.10.20.15 | Pass-the-Hash (SMB) | EID 4624 Type 3 NTLM |
| 01:38 | 10.10.5.23 | 10.10.20.16 | PsExec | EID 7045 + Zeek SMB |
| 01:45 | 10.10.20.16 | 10.10.20.17 | RDP | EID 4624 Type 10 |
| 02:00 | 10.10.20.17 | 10.10.20.15 | PsExec (triggered alert) | EID 7045 |
| 02:10 | 10.10.5.23 | 203.0.113.50 | Data Exfiltration | Zeek conn.log 2.3 GB |

### Affected Systems
- 10.10.5.23 (workstation-045) - Initial compromise
- 10.10.20.15 (file-server-01) - Data accessed
- 10.10.20.16 (app-server-02) - Pivoted through
- 10.10.20.17 (db-server-01) - Final target

### Detection Gaps
1. Initial phishing email not blocked by email gateway
2. Credential dumping not detected (no LSASS monitoring)
3. 30-minute gap between first lateral movement and alert
how to use detecting-lateral-movement-in-network

How to use detecting-lateral-movement-in-network on Cursor

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1

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 detecting-lateral-movement-in-network
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-lateral-movement-in-network

The skills CLI fetches detecting-lateral-movement-in-network from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-lateral-movement-in-network

Reload or restart Cursor to activate detecting-lateral-movement-in-network. Access the skill through slash commands (e.g., /detecting-lateral-movement-in-network) 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.

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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general reviews

Ratings

4.532 reviews
  • Chaitanya Patil· Dec 8, 2024

    Registry listing for detecting-lateral-movement-in-network matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Benjamin Dixit· Dec 8, 2024

    detecting-lateral-movement-in-network is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chen Desai· Dec 4, 2024

    detecting-lateral-movement-in-network reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yusuf Malhotra· Dec 4, 2024

    I recommend detecting-lateral-movement-in-network for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Piyush G· Nov 27, 2024

    Solid pick for teams standardizing on skills: detecting-lateral-movement-in-network is focused, and the summary matches what you get after install.

  • Anika Bhatia· Nov 27, 2024

    We added detecting-lateral-movement-in-network from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Layla Menon· Nov 23, 2024

    Useful defaults in detecting-lateral-movement-in-network — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Rahul Santra· Nov 7, 2024

    detecting-lateral-movement-in-network reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Pratham Ware· Oct 26, 2024

    detecting-lateral-movement-in-network is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Oct 18, 2024

    I recommend detecting-lateral-movement-in-network for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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