detecting-arp-poisoning-in-network-traffic

by mukul975

detecting-arp-poisoning-in-network-traffic helps detect ARP spoofing in live traffic or PCAPs using ARPWatch, Dynamic ARP Inspection, Wireshark, and Python checks. Built for incident response, SOC triage, and repeatable analysis of IP-to-MAC changes, gratuitous ARPs, and MITM indicators.

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AddedMay 11, 2026

CategoryIncident Response

Install Command

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-arp-poisoning-in-network-traffic

Curation Score

This skill scores 78/100, which means it is a solid listing candidate for directory users: it has a real ARP-poisoning detection workflow with enough specificity to be useful, though users should expect some operational gaps and likely need to adapt it to their environment.

78/100

Strengths

Clear cybersecurity scope and trigger context: the frontmatter and overview explicitly target ARP spoofing/poisoning detection in network traffic.
Real workflow assets: the repo includes a Python script plus an API reference with Scapy fields, indicators, and tool options like arpwatch and DAI.
Good install-decision value: the skill body is substantial, has no placeholder markers, and includes code fences and repository-linked references that support execution.

Cautions

No install command or setup path in SKILL.md, so users may need to infer dependencies and runtime steps.
The excerpt shows some truncated/partial sections, so edge-case handling and full end-to-end operating guidance may still be limited.

Network Security Arp Arp Spoofing Mitm Wireshark Scapy Python Security

Overview

Overview of detecting-arp-poisoning-in-network-traffic skill

What this skill does

The detecting-arp-poisoning-in-network-traffic skill helps you detect ARP spoofing and ARP poisoning in live traffic or packet captures. It is aimed at analysts who need to confirm a man-in-the-middle path, explain suspicious ARP changes, or build a repeatable detection workflow instead of relying on ad hoc packet inspection.

Who it is best for

Use this detecting-arp-poisoning-in-network-traffic skill if you are doing network defense, SOC triage, or detecting-arp-poisoning-in-network-traffic for Incident Response. It fits best when you already have a capture, switch access, or ARP monitoring source and need practical interpretation, not a generic theory refresher.

Why this skill is useful

The main value is layered detection: ARPWatch for host-level change tracking, Dynamic ARP Inspection for infrastructure enforcement, Wireshark for manual validation, and custom Python analysis for repeatable checks. That mix matters because ARP poisoning often shows up as small mapping anomalies, not a single obvious signature.

How to Use detecting-arp-poisoning-in-network-traffic skill

Install and load the skill

For detecting-arp-poisoning-in-network-traffic install, add it from the repository path and then inspect the skill files before you start analysis:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-arp-poisoning-in-network-traffic
Then read SKILL.md, references/api-reference.md, and scripts/agent.py first. Those files show the intended workflow, packet fields, and the detection logic the skill expects.

What input to provide

The detecting-arp-poisoning-in-network-traffic usage works best when you supply one of three inputs: a PCAP, a suspicious ARP event summary, or a network segment description with symptoms like IP conflicts, gateway instability, or repeated MAC changes. Strong input includes the capture window, affected hosts, whether the traffic is VLAN-scoped, and what “normal” looks like on that subnet.

A practical analysis workflow

Start by asking for a triage pass, then a deeper validation pass. For example: “Analyze this PCAP for ARP poisoning indicators, list the IP-to-MAC anomalies, separate false positives from likely spoofing, and recommend whether DAI, ARPWatch, or Wireshark is the best next step.” This makes the skill produce an investigation path instead of only a verdict.

What to inspect in the repo first

For fastest adoption, read references/api-reference.md to understand the detection indicators and scripts/agent.py to see how the analysis classifies replies, gratuitous ARPs, duplicate mappings, and MAC-to-IP relationships. If you plan to adapt the skill, those two files matter more than the repo structure.

detecting-arp-poisoning-in-network-traffic skill FAQ

Is this better than a normal prompt?

Yes, when you need consistent ARP-analysis structure. A generic prompt can identify spoofing, but the detecting-arp-poisoning-in-network-traffic skill helps you frame the work around concrete indicators such as duplicate mappings, ARP flip-flops, and gratuitous ARP floods.

Does it work for incident response?

Yes. For IR, this skill is strongest when you already suspect lateral movement or gateway impersonation and need evidence you can explain to responders. It is not a full incident workflow by itself, but it supports defensible scoping and validation.

What are the main limits?

It is focused on Layer 2 ARP behavior, so it will not detect every MITM technique, DNS poisoning, or encrypted traffic interception method. It also works best on local broadcast domains; if the issue is routed traffic or cloud networking, this skill may not be the right fit.

Is it beginner-friendly?

It is usable by beginners who can recognize a PCAP, an ARP table, or a suspicious host pair. Still, better results come from giving explicit context about the subnet, capture source, and the symptom you want confirmed.

How to Improve detecting-arp-poisoning-in-network-traffic skill

Provide cleaner network context

The best improvement to detecting-arp-poisoning-in-network-traffic output is specificity. Include the gateway IP, expected MAC addresses if known, switch model or DAI status, the time range, and whether the capture includes DHCP or onboarding events that could explain normal ARP churn.

Ask for the right kind of evidence

If you want high-signal results, request a split between “likely attack,” “benign explanation,” and “what to verify next.” That forces the skill to weigh indicators instead of overcalling every mapping change as spoofing.

Use the script logic as a validation target

When the first answer is too broad, re-run with the fields the repository’s scripts/agent.py emphasizes: ARP replies, gratuitous ARPs, duplicate IP-to-MAC mappings, and one MAC claiming multiple IPs. Those inputs help the detecting-arp-poisoning-in-network-traffic skill produce a more reproducible assessment.

Iterate from detection to remediation

After the first pass, ask for a follow-up that turns findings into action: isolate the suspect host, confirm switch protections, compare current ARP tables against baseline, and document whether DAI or ARPWatch should be enabled or tuned. That workflow makes the skill more useful for both hunting and containment.

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