Use when attacking AWS/Azure/GCP cloud — IAM/identity privilege escalation, IMDS/metadata SSRF, Entra device-code & PRT theft, GCP impersonation chains, Kubernetes/container escape, IaC/CI-CD federation abuse
npx skills add https://github.com/hypnguyen1209/offensive-claude --skill cloud-security
| Technique | ATT&CK | CWE | Reference | Script |
|-----------|--------|-----|-----------|--------|
| AWS IAM privesc (CreatePolicyVersion, PassRole, AttachPolicy) | T1098.001 | CWE-269 | references/aws-iam-privesc.md | scripts/aws_privesc_enum.py |
| AWS sts:AssumeRoot member-account escalation | T1078.004 | CWE-269 | references/aws-iam-privesc.md | scripts/aws_privesc_enum.py |
| Cross-account confused deputy / missing ExternalId | T1078.004 | CWE-441 | references/aws-iam-privesc.md | scripts/oidc_trust_auditor.py |
| IMDS / metadata SSRF credential theft (AWS/Azure/GCP) | T1552.005 | CWE-918 | references/imds-metadata-ssrf.md | scripts/imds_harvester.py |
| EKS node creds → IRSA / Pod Identity pivot | T1552.007 | CWE-668 | references/imds-metadata-ssrf.md | scripts/imds_harvester.py |
| Entra device-code phishing → PRT / device join | T1528 | CWE-287 | references/azure-entra-attacks.md | scripts/entra_device_code_phish.py |
| FOCI refresh-token family abuse | T1550.001 | CWE-613 | references/azure-entra-attacks.md | scripts/entra_device_code_phish.py |
| Azure Managed Identity / App-Admin → SP escalation | T1098.001 | CWE-269 | references/azure-entra-attacks.md | scripts/imds_harvester.py |
| GCP actAs + resource create impersonation chain | T1078.004 | CWE-269 | references/gcp-attacks.md | scripts/gcp_impersonation_mapper.py |
| GCP serviceAccountTokenCreator token chains | T1528 | CWE-269 | references/gcp-attacks.md | scripts/gcp_impersonation_mapper.py |
| Vertex AI P4SA / Ray head-node escalation | T1078.004 | CWE-732 | references/gcp-attacks.md | scripts/gcp_impersonation_mapper.py |
| Container escape (runc Leaky Vessels CVE-2024-21626) | T1611 | CWE-668 | references/kubernetes-container-escape.md | - |
| IngressNightmare (CVE-2025-1974) cluster takeover | T1190 | CWE-94 | references/kubernetes-container-escape.md | scripts/k8s_can_i_abuse.py |
| K8s RBAC privesc (pods/exec, token mount, node proxy) | T1078 | CWE-269 | references/kubernetes-container-escape.md | scripts/k8s_can_i_abuse.py |
| Terraform state secret extraction | T1552.001 | CWE-312 | references/iac-secrets-ci-cd.md | scripts/oidc_trust_auditor.py |
| OIDC federation trust-policy abuse (GitHub/TF Cloud) | T1199 | CWE-441 | references/iac-secrets-ci-cd.md | scripts/oidc_trust_auditor.py |
# --- 0. Identify where you are ---
aws sts get-caller-identity # AWS
az account show && az ad signed-in-user show # Azure
gcloud auth list && gcloud config get-value project # GCP
# --- 1. AWS: enumerate then map privesc paths ---
python3 scripts/aws_privesc_enum.py --profile compromised --json paths.json
cloudfox aws --profile compromised all-checks # alt: broad inventory
pacu # > run iam__enum_permissions ; run iam__privesc_scan
# --- 2. SSRF / metadata: harvest creds from a reachable compute target ---
python3 scripts/imds_harvester.py --ssrf "https://app/fetch?url=" --provider aws
python3 scripts/imds_harvester.py --local --provider azure --resource https://vault.azure.net/
# --- 3. Azure Entra: device-code phish for tokens (authorized phishing only) ---
python3 scripts/entra_device_code_phish.py --resource https://graph.microsoft.com \
--client-id 29d9ed98-a469-4536-ade2-f981bc1d605e # Auth Broker -> PRT path
# --- 4. GCP: build the service-account impersonation graph ---
python3 scripts/gcp_impersonation_mapper.py --project TARGET --out gcp_graph.json
# --- 5. Kubernetes: what can this token do, and can we break out? ---
python3 scripts/k8s_can_i_abuse.py --kubeconfig ./kubeconfig
kubectl auth can-i --list ; peirates
# --- 6. CI/CD + IaC: audit federation trust + dump state secrets ---
python3 scripts/oidc_trust_auditor.py --profile compromised
aws s3 cp s3://tf-state/prod/terraform.tfstate - | jq '.. | .password? // empty'
| Technique | Telemetry / IOC | Detection (Sigma / EDR / cloud) | OPSEC note |
|-----------|-----------------|---------------------------------|------------|
| IAM privesc API calls | CloudTrail CreatePolicyVersion, AttachUserPolicy, CreateLoginProfile | Alert on IAM write by non-IAM-admin principal; GuardDuty PrivilegeEscalation:IAMUser/* | Use existing admin sessions; avoid bulk enum that trips anomaly detection |
| sts:AssumeRoot | CloudTrail AssumeRoot (regional only) | Elastic "AssumeRoot by Rare User and Member Account" (new-terms rule) | Rare-event detection fires on first use per (principal, member account) |
| IMDS SSRF | VPC flow to 169.254.169.254 from web tier; STS use from new ASN | GuardDuty UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration* (creds used off-instance) | Use creds from same egress IP where possible; creds expire — refresh fast |
| Entra device-code phish | Sign-in logs authenticationProtocol=deviceCode; Auth Broker client 29d9ed98-...; new device registration | Sentinel device-code anomaly; CA "block device code flow" | Tokens valid even after password reset; device-join = MFA-resistant persistence |
| GCP impersonation | GenerateAccessToken / generateAccessToken in Admin Activity + Data Access logs | Alert on impersonation by principal lacking a documented chain | Requires Data Access (DATA_READ) audit logs enabled to see token minting |
| Container escape (runc) | New process from /proc/self/fd/* cwd; host binary writes; nsenter in container | Falco Container escape/Drop and execute new binary; runc ≤1.1.11 inventory | Overwrites host runc → noisy; prefer read-only host FS read for stealth |
| IngressNightmare | NGINX ingress pod loads .so from /proc; outbound from controller | Falco/Sysdig "IngressNightmare" shared-lib load; ingress-nginx < 1.11.5/1.12.1 | Exploit hits admission webhook (often internal-only) — low external noise |
| OIDC trust abuse | CloudTrail AssumeRoleWithWebIdentity from unexpected sub/repo | Alert on web-identity assume with mismatched aud/sub; RCP block | Wildcard sub (org:foo*) still exploitable; no creds needed |
sts:AssumeRoot, cross-account confused deputy / ExternalId, Cognito, secrets harvesting; detection per API.actAs + resource-create impersonation, serviceAccountTokenCreator chains, Cloud Functions takeover, Vertex AI ModeLeak/P4SA/Ray escalation.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 hypnguyen1209/cloud-security from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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