Network Appliance Offensive

Organization

transilienceai

network-appliance-offensive

Offensive testing of perimeter network appliances and VPN crypto — IKE/IPsec (aggressive-mode, transform/DH enum, NAT-T), Check Point SIC/OPSEC, safe firmware/patch-level inference for FortiGate/PAN-OS/Cisco ASA/Citrix feeding CVE applicability, NTLM Type-2 target-info decode, handshake-completion TLS-version probing, and an RST-TTL forgery discriminator. The active/offensive counterpart to the static firewall-review skill. Use when a perimeter firewall/VPN/appliance is in scope and you must test it (not just audit a config), without reinventing raw-socket protocol tooling each engagement.

Overview

Publisher	transilienceai
Repository	`communitytools`
Skill name	`network-appliance-offensive`
Stars	565
Forks	81
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by transilienceai on GitHub. Read the source before you install it.

Installation

Install the Network Appliance Offensive AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/transilienceai/communitytools/tree/main/skills/network-appliance-offensive
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/transilienceai/communitytools/tree/main/skills/network-appliance-offensive

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/network-appliance-offensive .claude/skills/network-appliance-offensive

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Network Appliance Offensive in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Network Appliance Offensive on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Network Appliance Offensive is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Network-Appliance Offensive

firewall-review audits a config statically; infrastructure covers generic ports/DNS/SMB. Neither tests a live perimeter appliance or its VPN crypto — so IKE builders, SIC/OPSEC fingerprinting, and TTL discriminators were reinvented from raw sockets each engagement, often shallowly, and CVE applicability was left UNDETERMINED across whole estates. This skill provides the deterministic, tested tools and the precondition-gated methodology. Non-destructive only — every tool observes/decodes/infers; none fires an exploit, and CVE applicability is a precondition check, never a blind "vulnerable."

Tools

Tool	Does	Anti-footgun
`tools/ike_enum.py`	IKEv1 aggressive-mode detection, IKEv1/IKEv2 transform + DH-group enum, NOTIFY / NAT-T decode (wraps `ike-scan`, raw ISAKMP SA_INIT builder fallback)	Aggressive-mode support (PSK-hash leak) is the finding; enumerate, don't crack in-band
`tools/checkpoint_sic_opsec.py`	Fingerprint SIC (18190/1), OPSEC LEA/ELA (18183/4), CA (18192/18210), FW1 (256/264), Gaia Portal; emit a CVE-precondition map incl. CVE-2024-24919	CVE-2024-24919 is `applicable` ONLY when the RA/Mobile-Access marker is observed — else `undetermined`
`tools/appliance_version_infer.py`	Safe firmware/patch-level inference for FortiGate / PAN-OS / Cisco ASA / Citrix from headers, login markers, cert CN/serial → CVE applicability	Never asserts `applicable` on a low-confidence / unknown version — returns `undetermined`
`tools/tls_handshake_probe.py`	Which TLS versions are supported, by completed handshake per pinned protocol	Fixes the `openssl s_client` exit/SECLEVEL false-positive — an aborted handshake ≠ support
`tools/ntlm_decode.py`	Decode an NTLM Type-2 (CHALLENGE) AV_PAIR block → NetBIOS/DNS host, domain, forest, OS build	Info-leak finding from an unauthenticated challenge; no auth attempted
`tools/perimeter_forensics.py`	RST-TTL forgery discriminator (real host vs firewall forging a RST) + IKE NOTIFY decode	Never asserts "internal host behind FW" on RSTs alone — a firewall forges RSTs indistinguishably at this layer; returns `undetermined` without an open-service TTL baseline

Workflow

Fingerprint the appliance — from the port set + banners + login markers, run appliance_version_infer.py (vendor + version guess + confidence) and, for Check Point, checkpoint_sic_opsec.py. A version guess of undetermined/low-confidence stays undetermined — do not score CVEs against it.
VPN crypto — on UDP 500/4500, run ike_enum.py: flag IKEv1 aggressive mode (leaks the PSK hash → a finding), enumerate accepted transforms + weak DH groups (1/2/5), and decode NAT-T/NOTIFY. IKEv2-only where IKEv1 RA is absent (a CVE requiring IKEv1 aggressive mode is not_applicable there — see severity-calibration.md rule 5).
TLS posture — tls_handshake_probe.py per host: report only handshake-completed versions; TLS 1.0/1.1 completion is the weak-protocol finding (not an openssl exit code).
Info leaks — decode any NTLM Type-2 challenge (ntlm_decode.py) for internal host/domain/OS intel; record it as an information-disclosure finding.
CVE applicability (precondition-gated) — for each surfaced appliance CVE, take the vendor+version from step 1 and the precondition map (checkpoint_sic_opsec / appliance_version_infer applicability) and mark applicable / undetermined / not_applicable. Enrich the CVE via python3 tools/nvd-lookup.py. An undetermined applicability is reported as such — never inflated to a confirmed vuln.
Forensics — if a "closed" host appears live behind the firewall, run perimeter_forensics.py --classify-ttls before asserting anything: distinguish a real host RST from a filter-forged RST (needs an open-service TTL baseline; otherwise undetermined).

Anti-Patterns

Do not score an appliance CVE as confirmed on a version banner alone — banners are backported/spoofable; gate on applicability (severity-calibration.md rules 4-5).
Do not claim TLS 1.0/1.1 support from an openssl s_client non-zero exit — require a completed handshake (tls_handshake_probe.py).
Do not assert "internal host behind the firewall" from RSTs — a firewall forges RSTs; use the TTL discriminator and accept undetermined.
Do not crack an IKE aggressive-mode PSK hash in-band on the engagement — capturing/flagging the aggressive-mode exposure is the finding; offline cracking needs explicit authorization.

Reference

Static counterpart: firewall-review (config audit). Generic network: infrastructure.
CVE precondition / applicability discipline: ../coordination/reference/severity-calibration.md (rules 4-5).

Frequently asked questions

What does the Network Appliance Offensive AI skill do?

Why use Network Appliance Offensive on TypingMind?

Because you install it once and use it with any model. Network Appliance Offensive is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Network Appliance Offensive in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/network-appliance-offensive. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Network Appliance Offensive?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Network Appliance Offensive?

As many as you like. As long as a model supports skills, you can use Network Appliance Offensive with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Network Appliance Offensive AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Network-Appliance Offensive

Tools

Workflow

Anti-Patterns

Reference

Frequently asked questions

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