auth
berglas
auth | berglas | |
---|---|---|
13 | 37 | |
826 | 1,224 | |
2.9% | 0.1% | |
7.6 | 6.9 | |
17 days ago | 3 days ago | |
TypeScript | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
auth
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Push code with GitHub Actions to Google Cloud’s Artifact Registry
This workflow will authenticate with Google Cloud using the Google Cloud auth GitHub Action and use Docker to authenticate and push to the registry. To make this workflow work (or flow?) we need to set up some Google Cloud resources and add in those values for our environment variables. Make sure to add in the value for PROJECT_ID where you have permission to create resources. The value for IMAGE_NAME can be anything — it’ll be created the first time this workflow runs:
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GitHub Actions could be so much better
The issue of integration with other tools is also quite strange. Of course, this is not directly related to github actions. For example, what needs to be done to use cloud run https://github.com/google-github-actions/auth#setting-up-wor...
- you must have the "bigquery.datasets.create" permission on the selected project
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IAM Best Practices [cheat sheet included]
While it is commonly associated with AWS, and their AWS IAM service, IAM is not limited to their platform. All cloud providers, such as Google Cloud and Azure DevOps, offer IAM solutions that allow users to access resources and systems. If you are looking for specific AWS IAM best practices, look no further than our AWS IAM Security Best Practices article:\ For the rest of this article, we will look at the generic best practices that have evolved over the last decade around each part of the basic question we started with, "who can access what?":
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How would I use Github Actions to run a Python Script to make changes to a Google Sheets Spreadsheet?
I found this but I don't quite get how it works. I haven't done all the steps yet but I get how to set it up. I just don't understand how this just magically authenticates future steps since my code still needs a token. Should I use this to authenticate the script? If so, how do I do it and what would I need in my code? If not what should I use instead?
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Cloud Incident Response
Cloud Identity and Access Management: This service provides fine-grained control over who has access to what resources within an organization's Google Cloud environment. It can be used to quickly revoke access to compromised accounts or limit access to sensitive resources. https://cloud.google.com/iam
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Advanced GitHub Actions - Conditional Workflow
I use google-github-actions/auth in the first step in my job to authenticate to GCP. At this point, I have 6 different GitHub secrets to test out the concept. Each branch has two secrets with the format BRANCH_WIP and BRANCH_SA.
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Learning Journal 3: Brainstorm a deployment process from GitHub to Google App Engine and Cloud SQL (Part 2)
There are 2 core parts authentication to GCP and App Engine deployment. Authentication is performed using auth, while a deployment uses deploy-appengine.
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CI/CD from GitHub to Google Cloud Platform(GAE)
You should have a look at using workload identity federation and OIDC tokens. There’s a guide on https://github.com/google-github-actions/auth It means you no longer need to hardcode service account credentials in GitHub secrets anymore.
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Learning Journal 2: Brainstorm a deployment process from GitHub to Google App Engine and Cloud SQL (Part 1)
Yes, there is a deploy-appengine action that automates the whole App Engine deployment process. Indeed, it uses gcloud commands underneath too. Either way, both approaches need an auth action to authenticate to GCP before any task can be performed.
berglas
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How to deploy a Django app to Google Cloud Run using Terraform
Secret Manager: secure storage for sensitive data e.g passwords.
- How do you handle sensitive variables with a service-worker?
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Increasing Your Cloud Function Development Velocity Using Dynamically Loading Python Classes
Google Secret Manager
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Getting started using Google APIs: API Keys (Part 2)
API keys are easy to "leak" or compromise, so best to not only use the restrictions presented to you when you create them but physically protect them as well. Don't code them in plain-text, don't check them into GitHub, etc. Store them in a secure database or use a service like GCP Secret Manager.
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Need some advice on API key storage
I've been looking at Google Secret Manager which sounds promising but I've not been able to find any examples or tutorials that help with the actual practical details of best practice or getting this working. I'm currently reading about Cloud Functions which also sound promising but again, I'm just going deeper and deeper into GCP without feeling like I'm gaining any useful insights.
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Secure GitHub Actions by pull_request_target
In this post, I described how to build secure GitHub Actions workflows by pull_request_target event instead of pull_request event. Using pull_request_target, you can prevent malicious codes from being executed in CI. And by managing secrets in secrets management services such as AWS Secrets Manager and Google Secret Manager and access them via OIDC, you can restrict the access to secrets securely. To migrate pull_request to pull_request_target, several modifications are needed. And pull_request_target has a drawback that it's difficult to test changes of workflows, so it's good to introduce pull_request_target to repositories that require strong permissions in CI. For example, a Terraform Monorepo tends to require strong permissions for CI, so it's good to introduce pull_request_target to it.
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Need Help with Deploying Directus on Google Cloud Platform (GCP)
If you want to make these secrets more secure and get versioning and access logs for them, you may want to switch to Secret Manager later on. They can still be exposed as environment variables to your code. It's a little more setup work, so start with the simple approach at the top.
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Has anyone been able to implement the OpenAI API with a Firebase Function (which is needed for the env variable API Key)?
https://cloud.google.com/secret-manager https://aws.amazon.com/secrets-manager/
- Securely storing Social Security Numbers with Firebase?
- Dónde van las credenciales cuando voy a subir un código a la nube para correr 24/7?
What are some alternatives?
Aegis - A free, secure and open source app for Android to manage your 2-step verification tokens.
kubernetes-external-secrets - Integrate external secret management systems with Kubernetes
angular-auth-oidc-client - npm package for OpenID Connect, OAuth Code Flow with PKCE, Refresh tokens, Implicit Flow
helm-charts
google-auth-library-nodejs - 🔑 Google Auth Library for Node.js
kube-secrets-init - Kubernetes mutating webhook for `secrets-init` injection
act - Run your GitHub Actions locally 🚀
gitleaks - Protect and discover secrets using Gitleaks 🔑
azure-pipelines-agent - Azure Pipelines Agent 🚀
cocert - Split and distribute your private keys securely amongst untrusted network
harden-runner - Network egress filtering and runtime security for GitHub-hosted and self-hosted runners
secrets-store-csi-driver-provider-gcp - Google Secret Manager provider for the Secret Store CSI Driver.