Cloud Privilege Escalation Awesome Script Suite 🚀🛡️

Welcome to the Cloud Privilege Escalation Awesome Script Suite – your one-stop solution to find your permissions whenever you compromise a principal in a Red Team across major cloud platforms: Azure, GCP, and AWS, plus Kubernetes. This suite is designed to help you determine all your permissions and also what it's possible to accomplish with them, focusing on privilege escalation and accessing sensitive information 🔥, and other potential attack vectors without modifying any resources.

This toolkit leverages advanced techniques to enumerate your permissions (it uses different permission enumeration techniques depending on the cloud) and utilizes insights from HackTricks Cloud and the shared permission categorizations maintained in that book to classify permissions as critical / high / medium / low.

The bundled categorizations for AWS, GCP, Azure, and Kubernetes update weekly from HackTricks Cloud. Each script also tries to download its latest YAML from this repository's main branch when it starts. A failed download falls back to the last valid cached copy, then the bundled copy. Pass --no-update-rules to use the bundled YAML without a network request. See maintaining the shared categorizations.

Permission ratings follow the shared severity policy, with audited source evidence.

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AzurePEAS 💼🖥️

AzurePEAS enumerates the effective permissions of a compromised user, service principal, or managed identity in Azure Resource Manager and Microsoft Entra ID. It performs no Azure write/delete operations and never executes commands inside workloads.

Enumeration order and fallbacks

1. Token claims (no directory permissions required): identifies the principal and records Graph scp, roles, and wids claims. These are useful even when every Graph enumeration endpoint returns 403.
2. Effective ARM permissions: asks Azure's permissions endpoint at every discovered subscription, resource group, resource, management group, and explicitly supplied scope. This does not require permission to list IAM role assignments.
3. IAM reconstruction: when allowed, lists transitive ARM/Entra assignments, role definitions, conditions, and PIM-eligible roles. Failure here does not stop other methods.
4. Resource discovery evidence: successful read-only subscription, resource-group, and resource calls are reported even when IAM cannot be read.
5. Azure CLI fallback: when --use-az-cli is used and ARM returns no permissions, AzurePEAS can probe Azure CLI commands that are demonstrably read-only and require no arguments. It uses no shell, disables dynamic extension installation and prompts, and skips unknown or potentially state-changing commands. Use --azure-services to make this faster or --skip-az-cli-fallback to disable it.

If resource listing is denied but you know an ARM resource ID from a VM's metadata, logs, scripts, or a previous foothold, pass it with --scopes. AzurePEAS will query that scope directly. This is the most reliable low-permission fallback.

Authentication

• Reuse an existing Azure CLI login: --use-az-cli (recommended when available).
• Supply ARM/Graph access tokens with arguments or AZURE_ARM_TOKEN / AZURE_GRAPH_TOKEN.
• Use device code with MFA by running without credentials.
• Use username/password or service-principal credentials with --use-username-password (legacy; no MFA).
• Supply an existing FOCI refresh token with --foci-refresh-token to try the optional M365 checks.

Tokens are never printed. Prefer environment variables over command-line arguments because command-line values may be visible in process listings. AZURE_PASSWORD and AZURE_FOCI_REFRESH_TOKEN are supported.

Known ARM and Microsoft Graph audiences for Azure public, US Government/DoD, and China clouds (plus legacy Germany ARM) are routed only to their matching trusted API host. Azure CLI authentication follows the cloud configured in az; optional FOCI/M365 features still depend on service availability in that cloud.

Microsoft 365 quick checks

With a usable FOCI refresh token, AzurePEAS performs bounded, read-only checks for SharePoint, OneDrive, Outlook, Teams, OneNote, contacts, and tasks. Use --not-enumerate-m365 to omit them. These checks indicate accessible data; they are not intended to download a complete tenant.

Examples

# Reuse the current az login and enumerate one subscription
python3 AzurePEAS.py --use-az-cli --check-only-these-subs <SUBSCRIPTION_ID>

# Tokens from the environment
export AZURE_ARM_TOKEN="<ARM_TOKEN>"
export AZURE_GRAPH_TOKEN="<GRAPH_TOKEN>"
python3 AzurePEAS.py --no-ask

# Resource-list fallback: check exact known scopes directly
python3 AzurePEAS.py --arm-token "<TOKEN>" \
  --check-only-these-subs <SUBSCRIPTION_ID> \
  --scopes "/subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RG>/providers/Microsoft.KeyVault/vaults/<VAULT>"

# Limit the last-resort CLI probes
python3 AzurePEAS.py --use-az-cli --azure-services vm,keyvault,storage

# ARM only, JSON report
python3 AzurePEAS.py --use-az-cli --skip-entraid --out-json-path azure-results.json

# Full option reference
python3 AzurePEAS.py --help

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GCPPEAS 🌐🔍

GCPPEAS enumerates the current principal's effective GCP permissions and highlights privilege-escalation and sensitive-access paths. Its transport rejects non-read-only endpoints: it never creates, changes, enables, executes, invokes, starts, stops, or deletes cloud resources.

How it works

GCPPEAS layers several independent techniques so a denied enumeration call does not stop the scan:

1. Permissionless/local clues: explicit --project, --service-account, and repeatable --resource values are always tested. --group preserves a known current-principal group membership when Cloud Identity discovery is denied. On a GCP workload, the metadata server supplies the current project, VM, and attached service account without IAM permissions. Credential and standard project environment variables are also used.
2. Container and resource discovery: Resource Manager search, Cloud Asset Inventory, and independent service-specific list calls discover projects, folders, organizations, VMs, Functions, buckets, service accounts, secrets, Cloud Run services/jobs, Artifact Registry repositories, Pub/Sub topics/subscriptions/snapshots, BigQuery datasets/tables/routines, Workflows, KMS key rings/keys, Cloud DNS managed zones, Agent Identity auth providers, and Integration Connector connections. Each failure is isolated and summarized; regional fallbacks continue when only some locations are denied. Connector discovery uses BASIC view and never requests stored authentication data.
3. Effective permission tests: Google's queryTestablePermissions supplies the current, resource-applicable catalog and testIamPermissions checks it in batches. These methods avoid parsing platform-dependent gcloud help output. If catalog lookup fails, GCPPEAS falls back to official predefined roles, a public catalog, and finally a built-in core set. Failed or partial tests are reported rather than silently treated as zero permissions.
4. IAM policy supplement: where getIamPolicy is allowed, direct/public/domain/known-group bindings and custom roles add context. Visible group bindings are reported without claiming that the group is joinable; the output points operators to the relevant Google Groups settings. Static grants are never merged over a successful effective test because IAM Deny, principal access boundaries, or request conditions may block them. If effective testing is unavailable, policy permissions are retained but clearly labeled as unverified. BigQuery datasets, which do not expose a dataset-level testIamPermissions method, use read-only metadata/list capability probes; BigQuery itself documents that table/routine tests can fail open.

Knowing a resource name is often enough to test permissions even when the principal cannot list its parent. Examples:

python3 GCPPEAS.py --project victim-project --only-specified
python3 GCPPEAS.py --resource gs://known-bucket --only-specified
python3 GCPPEAS.py --resource projects/victim-project/secrets/known-secret --only-specified
python3 GCPPEAS.py --resource projects/victim-project/datasets/known_dataset/tables/known_table --only-specified
python3 GCPPEAS.py --resource //run.googleapis.com/projects/victim-project/locations/us-central1/services/known-service --only-specified
python3 GCPPEAS.py --resource dns-zone:projects/victim-project/managedZones/known-zone --only-specified
python3 GCPPEAS.py --resource agent-auth-provider:projects/victim-project/locations/us-central1/authProviders/known-provider --only-specified
python3 GCPPEAS.py --resource integration-connector:projects/victim-project/locations/us-central1/connections/known-connection --only-specified

--billing-project only sets the quota-project header; GCPPEAS never enables an API or changes billing/IAM. Read calls and testIamPermissions can still appear in audit or access logs, so read-only does not mean invisible.

Authentication

Authentication follows the same fallback style as the other PEASS tools:

• --token, then CLOUDSDK_AUTH_ACCESS_TOKEN
• --sa-credentials-path, then GOOGLE_APPLICATION_CREDENTIALS
• Application Default Credentials, including workload metadata credentials

Examples:

# Existing gcloud user session
export CLOUDSDK_AUTH_ACCESS_TOKEN="$(gcloud auth print-access-token)"
python3 GCPPEAS.py --project victim-project --only-specified

# Service-account JSON
python3 GCPPEAS.py --sa-credentials-path credentials.json --project victim-project

# ADC / attached workload identity
python3 GCPPEAS.py

# Keep output locally for later analysis
python3 GCPPEAS.py --project victim-project --out-json-path gcp-results.json

GCPPEAS reports Workspace-capable OAuth scopes—including Gmail, Drive, Calendar, Admin SDK, Chat, Classroom, and editors—but does not automatically read Workspace content. A token containing both Workspace and Cloud scopes is called out as a cross-control-plane credential. Service-account impersonation/signing/key permissions distinguish directly shared Workspace resources (which do not require domain-wide delegation) from user impersonation through DWD. Complete Cloud DNS record-write pairs, user access to organization setIamPolicy, live-tested HTTP Workspace add-on deployment takeover, Agent Identity credential vaults, decrypted Application Integration auth profiles, and invoke-only Application Integration access receive concise GCP↔Workspace pivot notes with their required conditions and known-resource fallbacks.

Useful controls

• --only-specified: avoid broad container searches and scan only supplied projects/resources.
• --skip-iam-policies: use the permissionless testIamPermissions path without policy reads.
• --skip-bruteforce: policy-only mode; usually less complete.
• --skip-asset-inventory: use service-specific discovery fallbacks only.
• --resource: repeat for known resource names; comma-separated values are also accepted.
• --group: known current-principal group email(s), used when Cloud Identity membership lookup is denied.
• --billing-project: quota project only; no state change.
• --threads, --timeout, --retries: bound concurrency and transient failures.
• --proxy, --insecure, --debug: troubleshooting controls.

Run python3 GCPPEAS.py --help for the complete current option list.

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AWSPEAS ⚡️🔐

AWSPEAS enumerates the current AWS principal's permissions and highlights privilege-escalation and sensitive-access opportunities while leaving the target unchanged. It never calls AWS create, update, delete, invoke, run, start, send, or execute operations.

How It Works

AWSPEAS uses three permission-free-or-read-only fallbacks:

1. IAM policy reads: Reads the current user or role, all paginated inline/attached/group policies, managed-policy default versions, resource scopes, conditions, explicit denies, NotAction, NotResource, and permissions boundaries. If granular calls are blocked, it tries GetAccountAuthorizationDetails independently.

2. IAM simulation: If policy visibility is partial, empty, conditional, or boundary-limited, it tries SimulatePrincipalPolicy. Simulation is read-only and does not execute the tested actions. Because AWS otherwise evaluates resource-scoped actions against *, AWSPEAS uses explicitly supplied --resource-arn values and safely discovered Secrets Manager ARNs for a second resource-specific pass. Assumed-role sessions are resolved to their IAM role ARN when IAM permits it. The AWS action catalog falls back to installed botocore models when the online AWS catalog is unavailable.

3. Live read-only probes: If both methods above are incomplete, AWSPEAS tests read command families (List, Get, Describe, BatchGet, Head, Lookup, and Search) plus individually reviewed operations from other prefixes, such as FilterLogEvents, Scan, SelectResourceConfig, CheckNoPublicAccess, and ValidateTemplate. A prefix alone does not make an operation safe: calls that change resources or start jobs remain excluded. The AWS CLI is optional unless this fallback is needed. Help parsing supports plain, groff, and Unicode bullet formats, with botocore service models as an OS-independent fallback. Inventory reads run before dependent reads; successful JSON responses supply bounded resource IDs and other required parameters for later reads in the same service. This can confirm permissions that fabricated IDs cannot test. Large inventory and newly added scan probes use small, validated page sizes or one-item limits, and disable CLI auto-pagination where needed. AWSPEAS keeps only selected identifiers, redacts them from probe logs, and retains the existing dummy-ID fallback. For WorkMail and the WorkSpaces family, AWSPEAS can also recover option-matched resource IDs from successful CloudTrail history; failed-event parameters are rejected to prevent false-positive feedback loops. Use --aws-services to limit requests or --bruteforce-always to check for resource-policy access even after complete IAM reads.

Optional managed-policy inference can suggest unconfirmed permissions from successful live probes. Inferred permissions are labeled separately and must not be treated as confirmed.

AWS authorization can also depend on SCPs, RCPs, session policies, resource policies, VPC endpoint policies, request context, and service-specific behavior. AWSPEAS reports these limitations rather than presenting static policy statements as universally effective access.

Operational Security Considerations ⚠️

• Canary detection: AWSPEAS first tries to decode the account ID locally from modern AKIA/ASIA access-key IDs. This can stop before the first STS call for known canary accounts. It then checks the returned ARN/name for canary patterns. With --no-ask, a possible canary stops safely.
• CloudTrail: Read calls can still be logged. "Read-only" means no target state is intentionally changed, not that the scan is invisible.

Authentication & Execution Requirements

AWSPEAS accepts an AWS profile, explicit credentials, or the normal boto3 credential chain (environment variables, container/instance roles, web identity, and configured defaults). A region is optional; it uses the session/environment region and then us-east-1. The AWS CLI is needed only for live probes.

AWSPEAS Help & Usage

• Help:
  To view all the command options, run:

python3 ./AWSPEAS.py --help

usage: AWSPEAS.py [-h] [--profile PROFILE | --access-key-id ACCESS_KEY_ID]
                  [--secret-access-key SECRET_ACCESS_KEY] [--session-token SESSION_TOKEN]
                  [--region REGION] [--out-json-path OUT_JSON_PATH] [--threads THREADS]
                  [--debug] [--aws-services AWS_SERVICES] [--skip-iam-policies]
                  [--resource-arn RESOURCE_ARN]
                  [--skip-simulation] [--skip-bruteforce] [--bruteforce-always]
                  [--skip-managed-policies-guess] [--no-ask]

options:
  -h, --help            show this help message and exit
  --profile PROFILE     AWS profile (otherwise use the normal AWS credential chain)
  --access-key-id ACCESS_KEY_ID
                        AWS Access Key ID
  --secret-access-key SECRET_ACCESS_KEY
                        AWS Secret Access Key (required with --access-key-id)
  --session-token SESSION_TOKEN
                        AWS Session Token (optional, for temporary credentials)
  --out-json-path OUT_JSON_PATH
                        Output JSON file path (e.g. /tmp/aws_results.json)
  --threads THREADS     Number of threads to use
  --debug               Print more infromation when brute-forcing permissions
  --region REGION       Region for regional probes (optional)
  --aws-services AWS_SERVICES
                        Filter AWS services to brute-force permissions for indicating them as a comma separated list (e.g. --aws-services
                        s3,ec2,lambda,rds,sns,sqs,cloudwatch,cloudfront,iam,dynamodb)
  --resource-arn RESOURCE_ARN
                        Known ARN to test with resource-specific IAM simulation; repeat the option or pass comma-separated ARNs
  --skip-iam-policies   Skip retrieving permissions from IAM policies
  --skip-simulation     Skip simulating permissions using simulate-principal-policy
  --skip-bruteforce     Skip brute-force enumeration (automatic by default when IAM/simulation fail)
  --bruteforce-always   Run live read-only probes even when policy enumeration is complete
  --skip-managed-policies-guess
                        Skip guessing permissions based on AWS managed policies
  --no-ask              Do not prompt; use defaults (but stop safely on possible canary credentials)

• Usage Examples:

# Normal AWS credential chain (environment, role, or default profile)
python3 AWSPEAS.py

# Named profile; region is optional
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --no-ask

# Test a known resource ARN when it cannot be discovered with a safe List API
python3 AWSPEAS.py --profile <AWS_PROFILE> --resource-arn arn:aws:s3:::known-bucket --no-ask

# Using AWS credentials directly (Access Key + Secret Key)
python3 AWSPEAS.py --access-key-id <ACCESS_KEY_ID> --secret-access-key <SECRET_ACCESS_KEY> --region <AWS_REGION>

# Using temporary credentials (Access Key + Secret Key + Session Token)
python3 AWSPEAS.py --access-key-id <ACCESS_KEY_ID> --secret-access-key <SECRET_ACCESS_KEY> --session-token <SESSION_TOKEN> --region <AWS_REGION>

# Usage with specific AWS services (faster brute-force if needed)
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --aws-services s3,ec2,lambda,rds,sns,sqs,cloudwatch,cloudfront,iam,dynamodb

# Skip IAM and simulation, go directly to brute-force
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --skip-iam-policies --skip-simulation

# Only use IAM policies (skip simulation and brute-force)
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --skip-simulation --skip-bruteforce

# Try IAM and simulation, but never brute-force
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --skip-bruteforce

# Live read-only fallback only
python3 AWSPEAS.py --profile <AWS_PROFILE> --region <AWS_REGION> --skip-iam-policies --skip-simulation --skip-managed-policies-guess

# Also probe for read access granted by resource policies
python3 AWSPEAS.py --profile <AWS_PROFILE> --aws-services s3api,sqs,sns --bruteforce-always

---

K8sPEAS ☸️🔍

K8sPEAS enumerates the current Kubernetes identity's effective permissions and rates their security risk. It uses read-only HTTP GET requests and non-persisted self-review APIs. It never changes Kubernetes resources or uses exec, attach, port-forward, workload proxying, token minting, or dry-run write probes.

How it works

1. Identity and API discovery: asks the API server for the authenticated identity with SelfSubjectReview, then discovers available API groups, resources, subresources, and advertised verbs. When identity review is unavailable, local context and explicitly unverified token claims provide fallback information.
2. Namespace permission reviews: uses SelfSubjectRulesReview for known namespaces, preserving wildcards, special verbs, non-resource URLs, and resourceNames restrictions. Namespace names come from discovery, supplied --namespace values, and credential context.
3. Exact authorization checks: confirms high/critical findings with SelfSubjectAccessReview. When rules review is unavailable or empty, a bounded set of checks still provides coverage without requiring permission to list RBAC or workloads. These requests check authorization without performing the reviewed action.
4. RBAC and admission context: reads matching roles and bindings when allowed and inspects accessible Pod Security Admission labels, admission configuration, quotas, and policy objects to explain constraints. RBAC inventory is supplementary; failed reads do not stop permission reviews.
5. Optional exhaustive reviews: can check a matrix of discovered resources, verbs, subresources, and known namespaces. This phase can be slow and generate many audit events, so interactive runs ask first. --no-ask skips it unless --brute-force-permissions explicitly enables it; --skip-bruteforce disables it entirely.

Authentication

• Use the current kubeconfig context, or select one with --kubeconfig and --context.
• Supply a bearer token through K8S_TOKEN (preferred over --token to keep it out of process listings), together with --server and --certificate-authority.
• Use --client-certificate and --client-key together for client-certificate authentication.
• Run inside a Pod using its mounted service-account credentials.

The official Python Kubernetes client from requirements.txt is preferred. If unavailable, K8sPEAS falls back to kubectl with the same request allowlist.

Examples

# Review an existing kubeconfig context without exhaustive checks
python3 K8sPEAS.py --context my-context --skip-bruteforce

# Include known namespaces even when namespace listing is forbidden
python3 K8sPEAS.py --namespace demo --namespace staging --skip-bruteforce

# Noninteractive review with a complete JSON report
python3 K8sPEAS.py --no-ask --skip-bruteforce --out-json-path k8s-results.json

# Full option reference
python3 K8sPEAS.py --help

Interpreting results and useful controls

An allowed authorization review does not prove admission will accept a write. Incomplete reviews and evaluation errors remain explicit uncertainties. Grants for APIs or operations that are not currently served are retained as dormant findings, with their potential severity in JSON, and excluded from active high/critical totals.

The console summarizes findings; --show-all includes low-risk and repeated entries. --out-json-path preserves allowed, denied, and unknown reviews, discovery coverage, admission evidence, and inaccessible optional inventories. Use --threads, --timeout, and --retries to bound concurrency and transient failures. Read-only reviews and discovery calls can still appear in Kubernetes audit logs.

See the complete K8sPEAS guide and permission risk model for enumeration details, severity definitions, and limitations.