Privilege Escalation Assessment logo

Privilege Escalation Assessment

Organization
zero-lab-ai
privilege-escalation-assessment

Assesses and ranks Windows, Linux, and cloud privilege-escalation paths using observed prerequisites, execution authority, operational signal, reliability, risk, cleanup, and verification. Use after gaining an authorized foothold or cloud identity when deciding whether elevation is necessary and which candidate path is defensible.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill nameprivilege-escalation-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 Privilege Escalation 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/identity-security/skills/privilege-escalation-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/identity-security/skills/privilege-escalation-assessment .claude/skills/privilege-escalation-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Privilege Escalation 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 Privilege Escalation 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 Privilege Escalation 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.

Privilege escalation assessment

A candidate condition is not elevated access. Record the current principal, the required objective, the evidence for every prerequisite, and the exact identity proof produced after any authorized execution.

This skill evaluates vertical privilege transitions on one host, container, or cloud authority boundary. Use a directory or cloud attack-path workflow for multi-principal graph traversal, and a persistence workflow for durable access. Enumeration authorization does not authorize exploitation or state change.

When to Use

  • Assess privilege escalation for authorized security work.
  • 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 the subject, platform, current principal and privilege, desired privilege, authorization reference, monitoring context, and whether the mission actually requires elevation.
  2. Collect evidence with a source, locator, known timestamp or bounded observation window, and bounded summary. Do not invent precision when only a collection interval is known. Tool output is evidence input; its severity label is not a conclusion.
  3. Cover the common context categories and every category required for the platform. Preserve blocked and inconclusive areas instead of calling them clean.
  4. Build each candidate from explicit prerequisites. Mark each prerequisite met, unmet, or unknown and cite evidence for every met or unmet judgment.
  5. Derive candidate status: all prerequisites met is confirmed; any unmet is rejected; otherwise it is conditional. A product version alone does not establish exploitability; patch state and environmental compatibility remain separate prerequisites.
  6. State execution authority separately as authorized, approval-required, or prohibited. Describe state changes, operational signal, expected privilege, reliability, risk, complexity, cleanup, and likely detection artifacts before choosing a path.
  7. After an authorized attempt, record execution and verification evidence. Only a successful identity or effective-permission check establishes achieved privilege. Record failure and partial results without rewriting the candidate.

Interpretation rules

  • Prefer configuration and delegated-authority paths before vulnerable-code paths when they meet the objective with less risk.
  • Current membership, token privilege, file permission, or IAM permission proves only that condition. Preserve every downstream trigger and boundary.
  • A local administrator at medium integrity and an unprivileged user require different transitions; record the actual starting state.
  • Kernel and memory-corruption paths carry high failure impact. Require matching platform, patch, architecture, and lab evidence before ranking them as reliable.
  • A cloud permission name is not a path by itself. Resource scope, denies, permission boundaries, trust policy, and the resulting principal all matter.
  • If the current privilege already satisfies the mission, record that decision and do not select an escalation merely to complete a checklist.

Rationalizations to reject

  • "The scanner marked it exploitable." Confirm the underlying prerequisite chain; scanners report candidates and can miss patches, ACLs, or trigger state.
  • "The version is vulnerable." Version matching is one prerequisite, not proof of patch state, architecture compatibility, or successful execution.
  • "The file is writable, so we have root." The privileged consumer, trigger, execution context, and resulting identity still need evidence.
  • "Read-only authorization covers a harmless proof." Replacing files, changing policy, starting services, assuming roles, and invoking workloads are stateful actions that need explicit authority.
  • "The command returned success, so elevation worked." Verify the effective principal or permission in the new context and cite that evidence.
  • "Root is always the goal." Elevation that does not advance the mission adds signal, risk, and cleanup work without value.
  • "Cleanup can wait until the end." A path without a restoration plan is not ready to execute, especially when it changes services, tasks, files, or IAM.

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 Privilege Escalation Assessment AI skill do?

Assesses and ranks Windows, Linux, and cloud privilege-escalation paths using observed prerequisites, execution authority, operational signal, reliability, risk, cleanup, and verification. Use after gaining an authorized foothold or cloud identity when deciding whether elevation is necessary and which candidate path is defensible.

Why use Privilege Escalation Assessment on TypingMind?

Because you install it once and use it with any model. Privilege Escalation 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 Privilege Escalation Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/identity-security/skills/privilege-escalation-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 Privilege Escalation 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 Privilege Escalation Assessment?

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

Is the Privilege Escalation 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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