「etcd」の検索結果

Assess the security posture of Kubernetes etcd clusters by evaluating encryption at rest, TLS configuration, access controls, backup encryption, and network isolation.

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Prepare, validate, inspect, and edit EcoSIM soil management inputs using soil_mgmt_in NetCDF, editable Excel workbooks, and optional JSON files. Use when checking fertilizer, tillage, or irrigation management data; converting soil-mngt NetCDF to Excel for editing; converting edited Excel back to JSON or NetCDF; decoding fertf/tillf/irrigf selector variables; or validating EcoSIM ReadManagementMod.F90 soil-management inputs.

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Prepare, validate, and edit EcoSIM plant management inputs using pft_mgmt_in NetCDF, editable Excel workbooks, and optional PlantMgmtWriter.py JSON files. Use when creating or checking natural PFT management data, converting pft-mngt NetCDF to an Excel sheet for editing, converting edited Excel back to JSON or NetCDF, validating pft_type/pft_pltinfo/pft_mgmt entries, or preparing PlantMgmtWriter.py inputs for EcoSIM.

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Edit EcoSIM PFT-parameter NetCDF files such as ecosim_pftpar_20260303.nc with the ParEditor class from applications/notebooks/scripts/ParamEditor.py. Use when asked to modify, scale, copy, compare, or delete PFT parameter records for a plant functional type (six-character pfts code such as gr3s43 or ndlf34), update microbe parameter NetCDF values, or maintain the JSONL change log that records past parameter changes. Never modify plant_trait.*.desc files; they are read-only references.

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Build EcoSIM atmospheric greenhouse-gas forcing files by combining a historical monthly atmgas NetCDF with RCMIP/CMIP6 SSP concentration pathways. Use when Codex needs SSP245/SSP585 or other SSP atmospheric CO2, CH4, and N2O concentrations, key-year tables, or scenario NetCDF files spanning historical years through 2100 for EcoSIM or related biogeochemical forcing workflows.

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Extract one EcoSIM PFT parameter record from a PFT-parameter NetCDF file into a standalone NetCDF. Use when Codex needs to isolate all parameters for a given EcoSIM six-character PFT code such as ndlf34, or a four-character pfts_short value plus Koppen climate code, while preserving all non-PFT-specific variables such as pfts_short, pfts_long, and Koppen metadata.

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Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-with-kube-bench.

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Kubernetes cluster attack techniques covering the full attack lifecycle from initial foothold in a pod to cluster-wide compromise. Covers service account token theft and impersonation, RBAC misconfiguration exploitation including wildcard permissions and privilege escalation via role binding, direct etcd access for secret extraction, kubelet API abuse on port 10250 and read-only port 10255, pod escape via hostPID hostNetwork and hostPath volume mounts, Kubernetes secrets enumeration and decoding, admission controller bypass techniques, network policy bypass and lateral movement, cloud metadata service access from pods for credential theft on AWS EKS GCP GKE and Azure AKS, CRD and operator abuse for persistence, and node compromise via DaemonSet deployment. Tools include kubectl, kube-hunter, peirates, kubeaudit, kdigger, kubeletctl. Maps to MITRE ATT&CK T1609 Container Administration Command, T1610 Deploy Container, T1613 Container and Resource Discovery. Use this skill when assessing Kubernetes clusters, attacking from within a compromised pod, exploiting RBAC or kubelet misconfigurations, or performing cloud-native lateral movement.

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Hunt Kubernetes & Docker — API anonymous access, kubelet 10250 exec (SPDY/WebSocket, NOT plain POST) and the simpler /run primitive, etcd 2379 unauth, dashboard skip-login, RBAC misconfig, secret/SA-token abuse, docker.sock host escape, runc/container-escape (Leaky Vessels CVE-2024-21626), API-server-mediated nodes/proxy RCE, EphemeralContainers node-shell, bound/projected SA-token audience+expiry abuse, admission-controller bypass, Helm/Tiller remnants. Use when target runs containerized infra, exposes K8s ports (6443/10250/10255/2379/8443), or cloud metadata reveals K8s service accounts.

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Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.

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Kubernetes penetration testing playbook. Use when targeting Kubernetes clusters via API server, RBAC enumeration, service account abuse, etcd access, Kubelet API, pod escape, cloud-specific metadata, admission webhook bypass, and registry secrets.

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Read and process GLM output files. Use when you need to extract temperature data from NetCDF output, convert depth coordinates, or calculate RMSE against observations.

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Use BEFORE reading any data file that could be large (CSV/TSV, Parquet, HDF5, FITS, NetCDF, NDJSON, genomics FASTQ/FASTA/VCF/BAM, GRIB, ROOT, or big text/simulation logs like VASP OUTCAR). Returns a compact memory pointer — header/schema/shape/sample/key numbers — by introspection and sampling in bounded memory, so you never load a file bigger than the context window into the model. Reference data via the pointer; read specific ranges deterministically.

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mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XC

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Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read

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mdtraj molecular dynamics trajectory analysis (Python). Reads DCD/XTC/TRR/NetCDF/H5/PDB topologies and trajectories; computes RMSD vs time, radius of gyration, per-residue RMSF, residue-residue contact frequency maps, phi/psi torsions for Ramachandran plots (general + Gly/Pro), and 8-state DSSP secondary structure. Modules: trajectory I/O, geometry (distances/angles/dihedrals), structural analysis (RMSD/Rg/RMSF/SASA), contacts, hydrogen bonds, secondary structure (DSSP), NMR observables. For broader atom-selection grammar use mdanalysis-trajectory; for running MD simulations use OpenMM/GROMACS.

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Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.

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Kubernetes and container exploitation: identity/RBAC, secrets, kubelet/etcd, managed-cloud workload identities, container-to-node escape including CVE-2026-31431 Copy Fail, and Kubernetes-native persistence. Use when already in a Pod/container or holding kubeconfig. Own the chain to cluster-admin, node root, or a usable cloud identity. Operator chooses next modules. Endpoint blocks after OS execution hand off to /edr-bypass then return.

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Kubernetes penetration testing playbook. Use when targeting Kubernetes clusters via API server, RBAC enumeration, service account abuse, etcd access, Kubelet API, pod escape, cloud-specific metadata, admission webhook bypass, and registry secrets.

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Container and Kubernetes security assessment. Tests container escape vectors, Docker/containerd socket exposure, K8s RBAC misconfigurations, pod security violations, exposed API servers, etcd access, service account token abuse, image layer secrets, private registry attacks, SSRF to metadata services, cross-namespace network bypass, CIS benchmarks, crypto miner detection, resource exhaustion, and admission controller gaps. Both external and internal (compromised pod) perspectives. Uses trivy, kube-bench, kubectl, nuclei, dive, amicontained, and docker-bench-security. Covers OWASP Kubernetes Top 10 and all 22 Kubernetes Goat attack scenarios.

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Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read

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Kubernetes penetration testing systematically evaluates cluster security by simulating attacker techniques against the API server, kubelet, etcd, pods, RBAC, network policies, and secrets. Using tools

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Extract AmeriFlux BASE-BADM/BIF crop management records into EcoSIM-editable plant-management and soil-management Excel workbooks that can be converted to EcoSIM NetCDF files. Use when asked to derive EcoSIM planting, harvest, fertilizer, or tillage management inputs from AMF *_BIF_*.xlsx or BIF CSV files, especially for crop sites where management details live in GRP_DM_* records and DM_COMMENT fields.

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Run the combined AmeriFlux to EcoSIM extraction workflow for site metadata, NADP or tDEP atmospheric chemistry, gSSURGO soil data, and optional climate or grid NetCDF forcing. Use when building a complete site-specific EcoSIM input package.

日本語の概要は準備中です。原文の説明を表示しています。