detecting-lateral-movement-in-network
by mukul975detecting-lateral-movement-in-network helps detect post-compromise lateral movement in enterprise networks using Windows event logs, Zeek telemetry, SMB, RDP, and SIEM correlation. It is useful for threat hunting, incident response, and detecting-lateral-movement-in-network for Security Audit reviews with practical detection workflows.
This skill scores 78/100 and is worth listing: it has a clearly defined cybersecurity use case, substantial workflow content, and an included Python helper that can parse Zeek/Windows evidence. Directory users should still expect some implementation-specific setup work because the repo does not provide an install command or very explicit end-to-end onboarding flow.
- Clear detection focus for lateral movement hunting across Windows events, Zeek logs, SMB, and RDP evidence.
- Substantial operational content with named event IDs, log sources, and example Zeek/Splunk queries plus a helper script.
- Good triggerability from the SKILL metadata and 'When to Use' section, which narrows the skill to concrete incident-response and hunting scenarios.
- No install command in SKILL.md, so users may need to wire the skill into their environment manually.
- The workflow is useful but not fully self-serve; some prerequisites and integration details are implied rather than fully documented.
Overview of detecting-lateral-movement-in-network skill
What this skill does
The detecting-lateral-movement-in-network skill helps you detect attacker movement inside a network after initial compromise. It focuses on practical signals such as Windows authentication events, Zeek network telemetry, SMB, RDP, and SIEM correlation so you can turn noisy internal activity into actionable detections.
Who it is for
Use the detecting-lateral-movement-in-network skill if you are doing detection engineering, incident response, threat hunting, or a detecting-lateral-movement-in-network for Security Audit review of east-west traffic. It is most useful when you already have log access and need better triage rules, not when you are looking for a pure EDR replacement.
Why it is different
This skill is geared toward operational detection, not theory. The repo includes a supporting Python agent and reference material for event IDs, Zeek logs, and query patterns, which makes it easier to move from idea to implementation. That also means the skill works best when you can provide real log sources and a target environment.
How to Use detecting-lateral-movement-in-network skill
Install and inspect the repo
For detecting-lateral-movement-in-network install, use:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-lateral-movement-in-network
After install, read SKILL.md first, then references/api-reference.md, then scripts/agent.py. Those files show the event mappings, log assumptions, and the executable logic the skill is built around. If you need the quickest path, search for sections on prerequisites, workflow, and detection examples.
Give it the right input
The detecting-lateral-movement-in-network usage pattern works best when you specify:
- log sources you have: Windows Security logs, Zeek
conn.log,smb_mapping.log,kerberos.log, SIEM data - the behavior you suspect: PsExec, RDP hopping, pass-the-hash, WMI, service creation
- scope: one host, a subnet, or an incident time window
- output format: detection ideas, validation checklist, or SIEM query draft
A weak prompt says: “find lateral movement.” A stronger one says: “build detections for lateral movement using Windows 4624/4648/7045 and Zeek east-west traffic for a Windows domain during the last 24 hours.”
Follow a practical workflow
A good detecting-lateral-movement-in-network guide is:
- Confirm what telemetry exists and what is missing.
- Map the suspected behavior to event IDs and network logs.
- Baseline normal internal communication before hunting anomalies.
- Convert the suspicious pattern into a SIEM rule or hunt query.
- Validate against benign admin activity so you do not over-alert.
If you only have north-south logs, the skill will be limited. It is built for internal movement detection, so east-west visibility matters more than broad perimeter monitoring.
What to read first for better output
Start with references/api-reference.md for the Windows event IDs and Zeek log names the skill expects. Then inspect scripts/agent.py to see how it classifies suspicious internal connections and logon types. That usually gives more usable output than reading the whole repo at once.
detecting-lateral-movement-in-network skill FAQ
Is this just a prompt or a real skill?
It is a real detecting-lateral-movement-in-network skill with repository structure, reference material, and a Python helper. That makes it more dependable than a generic prompt when you need repeatable detection logic.
Do I need security tooling already in place?
Yes, at least some telemetry. The skill is strongest with Windows event logs, Zeek, and SIEM access. If you do not have internal network visibility, the results will be weaker and more speculative.
Is it beginner friendly?
It is usable by beginners who can describe their environment and logs, but the best results come from users who know which systems, ports, and authentication events they want to inspect. If you are new, start with one suspected technique instead of asking for broad hunting coverage.
When should I not use it?
Do not use it for endpoint forensics without network or log context, or for generic malware analysis unrelated to lateral movement. It is also a poor fit if you only want a high-level explanation with no detection output.
How to Improve detecting-lateral-movement-in-network skill
Provide concrete telemetry and a time window
The biggest quality gain comes from naming the actual sources and interval. Say whether you have Zeek conn.log, Windows 4624/4625/4648/7045, or SIEM exports, and include the time range. That helps the skill avoid broad recommendations and focus on evidence you can actually validate.
Name the lateral movement path you care about
If you want better detecting-lateral-movement-in-network usage, specify the technique: RDP, PsExec, SMB admin shares, WMI, Kerberos abuse, or pass-the-hash. Each one maps to different signals, and the skill can produce sharper detections when it knows the likely path.
Ask for output that can be acted on
Instead of asking for “analysis,” request one of these:
- a hunt query for Splunk or another SIEM
- a shortlist of high-signal event IDs
- a validation plan for benign admin activity
- a triage checklist for a suspicious host pair
This improves the chance that the output supports Security Audit work instead of reading like a summary.
Iterate against false positives
The main failure mode in detecting lateral movement is over-triggering on admin tools and normal remote support activity. If the first result is too noisy, feed back what is legitimate in your environment: jump hosts, patching tools, known service accounts, or admin subnets. That lets the skill narrow the rule set instead of widening it.