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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondetecting-lateral-movement-in-networkExecute the skills CLI command in your project's root directory to begin installation:
Fetches detecting-lateral-movement-in-network from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate detecting-lateral-movement-in-network. Access via /detecting-lateral-movement-in-network in your agent's command palette.
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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| 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 |
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.
# 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
# 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: '$'
condition: selection and not filter
timeframe: 15m
count:
field: ComputerName
min: 3
group-by: TargetUserName
level: high
tags:
- attack.lateral_movement
- attack.t1550.002
EOF
# Convert Sigma rule to Splunk SPL
sigma convert -t splunk lateral_movement_pth.yml
# Convert to Elastic query
sigma convert -t elasticsearch lateral_movement_pth.yml
# Detect SMB lateral movement (admin$ 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
# 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
# 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
# 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
| Term | Definition |
|---|---|
| Lateral Movement | MITRE 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 Traffic | Network communication between internal systems (as opposed to north-south traffic between internal and external networks) |
| Authentication Anomaly | Deviation 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 |
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:
Pitfalls:
## 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
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
Registry listing for detecting-lateral-movement-in-network matched our evaluation — installs cleanly and behaves as described in the markdown.
detecting-lateral-movement-in-network is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
detecting-lateral-movement-in-network reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend detecting-lateral-movement-in-network for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: detecting-lateral-movement-in-network is focused, and the summary matches what you get after install.
We added detecting-lateral-movement-in-network from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in detecting-lateral-movement-in-network — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
detecting-lateral-movement-in-network reduced setup friction for our internal harness; good balance of opinion and flexibility.
detecting-lateral-movement-in-network is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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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