Protocol Exploitation Assessment logo

Protocol Exploitation Assessment

Organization
zero-lab-ai
protocol-exploitation-assessment

Assesses network protocol exploitation candidates while separating observed service behavior, advisory applicability, reproduced faults, demonstrated impact, authorization, disruption risk, telemetry, and coverage. Use when a banner, scanner result, packet trace, reset, legacy protocol, or suspected handshake weakness needs a defensible validation plan.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill nameprotocol-exploitation-assessment
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
Links
  • 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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Protocol Exploitation Assessment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/protocol-exploitation-assessment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/network-protocol-security/skills/protocol-exploitation-assessment .claude/skills/protocol-exploitation-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Protocol Exploitation Assessment 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 Protocol Exploitation Assessment 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 Protocol Exploitation Assessment 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.

Protocol exploitation assessment

A banner can identify a candidate. It cannot prove the running implementation, patch state, vulnerable behavior, or security impact. Preserve each proof layer.

This skill plans and records assessment. It does not provide exploit commands, payloads, credentials, poisoning, relay, coercion, replay, fuzz cases, or target contact.

When to Use

  • A banner, scanner result, packet trace, reset, legacy protocol, or suspected handshake weakness needs a defensible validation plan.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record exact targets, assessment window, disruption ceiling, signal ceiling or null, and written authorization by action class. Preserve approval gates and prohibitions separately.
  2. Inventory each service from evidence. Keep transport reachability, protocol identity, implementation identity, version, configuration, and patch state as separate claims. Unknown stays null.
  3. Store evidence provenance, trust, bounded or unknown time, subject, summary, and limitations. Historical notes and scanner labels remain low-trust context.
  4. Classify candidates by protocol weakness family. Link advisories without equating a version-range match with applicability.
  5. Separate candidate proof into: service observation, weakness evidence, reproduced behavior, and demonstrated impact. A socket reset needs independent service-health or crash evidence before it can prove a fault.
  6. Define possible next actions by class, target, prerequisites, disruption, request count, expected telemetry, stop conditions, authorization, and cleanup.
  7. Derive action state and candidate state. Recommend the lowest cost authorized action whose prerequisites are met and that adds evidence.
  8. Mark a candidate finding-ready only after the weakness is demonstrated. Keep advisory matches and leads in the open-work ledger.
  9. Cover service identity, implementation state, authentication, cryptography, state machine, input handling, disclosure, and telemetry/recovery for every service. Blocked and not-applicable work remains visible.

Boundary with adjacent skills

  • Use network-recon to discover hosts and services before assessing candidates.
  • Use protocol-traffic-analysis to interpret supplied packet captures without active validation.
  • Use binary-fault-triage for crash and binary-analysis depth after a reproducible protocol fault exists.
  • Use binary-fault-validation-readiness to decide whether a confirmed fault has the prerequisites for exploit development.
  • Use findings-report after this skill marks a candidate finding-ready.
  • Use detection-validate when the objective is proving a detector rather than finding protocol weaknesses.

Rationalizations to reject

  • "The version is in the affected range." Vendors backport fixes and banners lie. This proves a match, not the running code's vulnerability.
  • "The connection reset, so the service crashed." A peer, proxy, timeout, or parser rejection can reset one socket while the service stays healthy.
  • "The scanner says vulnerable." Preserve the scanner output as a lead and inspect what evidence the check actually collected.
  • "A protocol is legacy, so exploitation is proven." Legacy posture can be a real exposure without proving a particular attack path.
  • "Fuzzing is just another request." Mutation campaigns have aggregate load and outage risk that require explicit authorization and recovery controls.
  • "No alert means the test was quiet." Unknown sensor health and an unreviewed query window cannot support a detection conclusion.
  • "Production is the only realistic validation." A faithful lab, build provenance, or configuration record may answer the question with less risk.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Protocol Exploitation Assessment AI skill do?

Assesses network protocol exploitation candidates while separating observed service behavior, advisory applicability, reproduced faults, demonstrated impact, authorization, disruption risk, telemetry, and coverage. Use when a banner, scanner result, packet trace, reset, legacy protocol, or suspected handshake weakness needs a defensible validation plan.

Why use Protocol Exploitation Assessment on TypingMind?

Because you install it once and use it with any model. Protocol Exploitation Assessment 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 Protocol Exploitation Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/protocol-exploitation-assessment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Protocol Exploitation Assessment?

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 Protocol Exploitation Assessment?

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

Is the Protocol Exploitation Assessment AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 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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