Cloud Attack Path logo

Cloud Attack Path

Organization
zero-lab-ai
cloud-attack-path

Maps cloud identity and authorization paths across AWS, Azure, GCP, and hybrid trust boundaries, separating proven access from plausible and blocked routes. Use during an authorized cloud penetration test when tracing how a starting identity could assume roles, use credentials, execute workloads, cross providers, or reach sensitive data.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namecloud-attack-path
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 Cloud Attack Path 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/application-endpoint-security/skills/cloud-attack-path
  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/application-endpoint-security/skills/cloud-attack-path .claude/skills/cloud-attack-path
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloud Attack Path 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 Cloud Attack Path 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 Cloud Attack Path 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.

Cloud attack paths

Cloud attack paths are identity graphs. A principal reaches an objective through permissions, credentials, workload identities, federation, and resource policy. An allow statement is one input to that graph, not proof that the edge works.

Use this skill only inside written authorization. Mapping a possible path does not authorize credential retrieval, role assumption, deployment, data access, or any other validation action.

When to Use

  • An authorized cloud penetration test when tracing how a starting identity could assume roles, use credentials, execute workloads, cross providers, or reach sensitive data.
  • 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.

Build the map

  1. Define the endpoints. State the starting identity, target resource or data, the cloud boundaries in scope, and the smallest proof the engagement requires.
  2. Record evidence before conclusions. Classify each source as observed, reported current, reported stale, or inferred. Names such as prod-admin-key suggest provenance; they do not establish it.
  3. Normalize the graph. Represent identities, credentials, services, resources, and data as nodes. Represent authorization, impersonation, credential material, federation, workload execution, and resource access as directed edges.
  4. Resolve effective access. Account for identity and resource policies, explicit denies, conditions, permission boundaries, session restrictions, organization policy, encryption controls, service enablement, and network or perimeter controls that apply. Keep unresolved layers visible.
  5. Classify every edge. Use:
    • validated when current evidence establishes the transition;
    • candidate when the transition is plausible but has unverified facts;
    • blocked when a known control prevents it;
    • unknown when available evidence cannot support a direction.
  6. Make boundary crossings explicit. For every account, tenant, project, provider, or on-premises crossing, name the trust mechanism and its evidence. Credential files, federation, and workload identity are separate transitions.
  7. Assemble continuous paths. Each edge must begin where the previous edge ended. Include blocked branches because they explain why apparent privilege does not produce a route.
  8. Plan minimum validation. Prefer metadata and policy reads. Classify each validation step and compare it with written authorization. Mark credential reads, content access, active execution, and state changes as requiring separate approval when they are not already allowed.
  9. State only what the path proves. A path is:
    • proven only when every edge is validated;
    • plausible when at least one edge is a candidate and none is blocked or unknown;
    • blocked when any edge is blocked;
    • unknown when uncertainty prevents a stronger result.

Provider semantics to preserve

  • AWS: an identity allow may still lose to an SCP, permissions boundary, session policy, resource policy, KMS policy, condition, or explicit deny. PassRole needs a permitted service action that can consume the role.
  • Azure: distinguish Entra directory roles from Azure resource RBAC. Resolve assignment scope, deny assignments, managed identities, vault mode, and conditional access or network restrictions relevant to the transition.
  • GCP: serviceAccounts.actAs lets a workload run as a service account; it does not itself mint a token. Resolve allow and deny policies, conditions, service enablement, service agents, and perimeter controls.
  • Hybrid and multi-cloud: treat a credential or assertion as one node and the identity it may represent as another. The mapping between them needs its own evidence.

Judgment calls

The script cannot interpret live provider policy or determine whether a counterfactual deployment would succeed. It verifies the normalized record: references, continuity, evidence strength, boundary declarations, deny precedence, authorization classes, and claim strength. A clean result means the assessment is honest about its inputs, not that the attack path is executable.

Rationalizations to reject

ThoughtReality
"The policy says Allow, so the edge is proven."Effective access is the result of every applicable policy and condition. Record unresolved layers.
"The secret name tells us which identity it belongs to."A name is inferred provenance. Use a key ID, application ID, deployment record, or other non-secret evidence.
"PassRole means I can become that role."It only matters with an allowed service action that accepts the role.
"actAs is direct impersonation."GCP workload attachment and token creation are different permissions.
"We can deploy a harmless function to verify it."Deployment changes state and creates telemetry. Authorization decides whether it is permitted.
"The final object permission proves the whole chain."Every preceding identity and trust transition must also hold.
"the record is complete, so the path works."It passed structural and evidentiary checks over supplied facts. Provider behavior still requires authorized validation.

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 Cloud Attack Path AI skill do?

Maps cloud identity and authorization paths across AWS, Azure, GCP, and hybrid trust boundaries, separating proven access from plausible and blocked routes. Use during an authorized cloud penetration test when tracing how a starting identity could assume roles, use credentials, execute workloads, cross providers, or reach sensitive data.

Why use Cloud Attack Path on TypingMind?

Because you install it once and use it with any model. Cloud Attack Path 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 Cloud Attack Path in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/application-endpoint-security/skills/cloud-attack-path. 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 Cloud Attack Path?

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 Cloud Attack Path?

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

Is the Cloud Attack Path 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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