Search results for GCP IAM

Clean up of over permissioned IAM accounts on GCP infra in an automated way CureIAM is an easy-to-use, reliable, and performant engine for Least Privilege Principle Enforcement on GCP cloud infra. It enables DevOps and Security team to quickly clean up accounts in GCP infra that have granted permissions of more than what are required. CureIAM fetches the recommendations and insights from GCP IAM recommender, scores them and enforce those recommendations automatically on daily basic. It takes care of scheduling and all other aspects of running these enforcement jobs at scale. It is built on top of GCP IAM recommender APIs and Cloudmarker framework. Key features Discover what makes CureIAM scalable and production grade. Config driven : The entire workflow of CureIAM is config driven. Skip to Config section to know more about it. Scalable : Its is designed to scale because of its plugin driven, multiprocess and multi-threaded approach. Handles Scheduling: Scheduling part is embedded in CureIAM code itself, configure the time, and CureIAM will run daily at that time note. Plugin driven: CureIAM codebase is completely plugin oriented, which means, one can plug and play the existing plugins or create new to add more functionality to it. Track actionable insights: Every action that CureIAM takes, is recorded for audit purpose, It can do that in file store and in elasticsearch store. If you want you can build other store plugins to push that to other stores for tracking purposes. Scoring and Enforcement: Every recommendation that is fetch by CureIAM is scored against various parameters, after that couple of scores like safe_to_apply_score, risk_score, over_privilege_score. Each score serves a different purpose. For safe_to_apply_score identifies the capability to apply recommendation on automated basis, based on the threshold set in CureIAM.yaml config file. Usage Since CureIAM is built with python, you can run it locally with these commands. Before running make sure to have a configuration file ready in either of /etc/CureIAM.yaml, ~/.CureIAM.yaml, ~/CureIAM.yaml, or CureIAM.yaml and there is Service account JSON file present in current directory with name preferably cureiamSA.json. This SA private key can be named anything, but for docker image build, it is preferred to use this name. Make you to reference this file in config for GCP cloud. # Install necessary dependencies$ pip install -r requirements.txt# Run CureIAM now$ python -m CureIAM -n# Run CureIAM process as schedular$ python -m CureIAM# Check CureIAM help$ python -m CureIAM --help CureIAM can be also run inside a docker environment, this is completely optional and can be used for CI/CD with K8s cluster deployment. # Build docker image from dockerfile$ docker build -t cureiam . # Run the image, as schedular$ docker run -d cureiam # Run the image now$ docker run -f cureiam -m cureiam -n Config CureIAM.yaml configuration file is the heart of CureIAM engine. Everything that engine does it does it based on the pipeline configured in this config file. Let's break this down in different sections to make this config look simpler. Let's configure first section, which is logging configuration and scheduler configuration. logger: version: 1 disable_existing_loggers: false formatters: verysimple: format: >- [%(process)s] %(name)s:%(lineno)d - %(message)s datefmt: "%Y-%m-%d %H:%M:%S" handlers: rich_console: class: rich.logging.RichHandler formatter: verysimple file: class: logging.handlers.TimedRotatingFileHandler formatter: simple filename: /tmp/CureIAM.log when: midnight encoding: utf8 backupCount: 5 loggers: adal-python: level: INFO root: level: INFO handlers: - rich_console - file schedule: "16:00" This subsection of config uses, Rich logging module and schedules CureIAM to run daily at 16:00. Next section is configure different modules, which we MIGHT use in pipeline. This falls under plugins section in CureIAM.yaml. You can think of this section as declaration for different plugins. plugins: gcpCloud: plugin: CureIAM.plugins.gcp.gcpcloud.GCPCloudIAMRecommendations params: key_file_path: cureiamSA.json filestore: plugin: CureIAM.plugins.files.filestore.FileStore gcpIamProcessor: plugin: CureIAM.plugins.gcp.gcpcloudiam.GCPIAMRecommendationProcessor params: mode_scan: true mode_enforce: true enforcer: key_file_path: cureiamSA.json allowlist_projects: - alpha blocklist_projects: - beta blocklist_accounts: - foo@bar.com allowlist_account_types: - user - group - serviceAccount blocklist_account_types: - None min_safe_to_apply_score_user: 0 min_safe_to_apply_scor e_group: 0 min_safe_to_apply_score_SA: 50 esstore: plugin: CureIAM.plugins.elastic.esstore.EsStore params: # Change http to https later if your elastic are using https scheme: http host: es-host.com port: 9200 index: cureiam-stg username: security password: securepassword Each of these plugins declaration has to be of this form: plugins: <plugin-name>: plugin: <class-name-as-python-path> params: param1: val1 param2: val2 For example, for plugins CureIAM.stores.esstore.EsStore which is this file and class EsStore. All the params which are defined in yaml has to match the declaration in __init__() function of the same plugin class. Once plugins are defined , next step is to define how to define pipeline for auditing. And it goes like this: audits: IAMAudit: clouds: - gcpCloud processors: - gcpIamProcessor stores: - filestore - esstore Multiple Audits can be created out of this. The one created here is named IAMAudit with three plugins in use, gcpCloud, gcpIamProcessor, filestores and esstore. Note these are the same plugin names defined in Step 2. Again this is like defining the pipeline, not actually running it. It will be considered for running with definition in next step. Tell CureIAM to run the Audits defined in previous step. run: - IAMAudits And this makes the entire configuration for CureIAM, you can find the full sample here, this config driven pipeline concept is inherited from Cloudmarker framework. Dashboard The JSON which is indexed in elasticsearch using Elasticsearch store plugin, can be used to generate dashboard in Kibana. Contribute [Please do!] We are looking for any kind of contribution to improve CureIAM's core funtionality and documentation. When in doubt, make a PR! Credits Gojek Product Security Team Demo <> ============= NEW UPDATES May 2023 0.2.0 Refactoring Breaking down the large code into multiple small function Moving all plugins into plugins folder: Esstore, files, Cloud and GCP. Adding fixes into zero divide issues Migration to new major version of elastic Change configuration in CureIAM.yaml file Tested in python version 3.9.X Library Updates Adding the version in library to avoid any back compatibility issues. Elastic==8.7.0 # previously 7.17.9 elasticsearch==8.7.0 google-api-python-client==2.86.0 PyYAML==6.0 schedule==1.2.0 rich==13.3.5 Docker Files Adding Docker Compose for local Elastic and Kibana in elastic Adding .env-ex change .env-ex to .env to before running the docker Running docker compose: docker-compose -f docker_compose_es.yaml up Features Adding the capability to run scan without applying the recommendation. By default, if mode_scan is false, mode_enforce won't be running. mode_scan: true mode_enforce: false Turn off the email function temporarily. Download CureIAM

CureIAM GCP IAM GCP Infra Principle Enforcement

ZeusCloud is an open source cloud security platform. Discover, prioritize, and remediate your risks in the cloud. Build an asset inventory of your AWS accounts. Discover attack paths based on public exposure, IAM, vulnerabilities, and more. Prioritize findings with graphical context. Remediate findings with step by step instructions. Customize security and compliance controls to fit your needs. Meet compliance standards PCI DSS, CIS, SOC 2, and more! Quick Start Clone repo: git clone --recurse-submodules git@github.com:Zeus-Labs/ZeusCloud.git Run: cd ZeusCloud && make quick-deploy Visit http://localhost:80 Check out our Get Started guide for more details. A cloud-hosted version is available on special request - email founders@zeuscloud.io to get access! Sandbox Play around with our sandbox environment to see how ZeusCloud identifies, prioritizes, and remediates risks in the cloud! Features Discover Attack Paths - Discover toxic risk combinations an attacker can use to penetrate your environment. Graphical Context - Understand context behind security findings with graphical visualizations. Access Explorer - Visualize who has access to what with an IAM visualization engine. Identify Misconfigurations - Discover the highest risk-of-exploit misconfigurations in your environments. Configurability - Configure which security rules are active, which alerts should be muted, and more. Security as Code - Modify rules or write your own with our extensible security as code approach. Remediation - Follow step by step guides to remediate security findings. Compliance - Ensure your cloud posture is compliant with PCI DSS, CIS benchmarks and more! Why ZeusCloud? Cloud usage continues to grow. Companies are shifting more of their workloads from on-prem to the cloud and both adding and expanding new and existing workloads in the cloud. Cloud providers keep increasing their offerings and their complexity. Companies are having trouble keeping track of their security risks as their cloud environment scales and grows more complex. Several high profile attacks have occurred in recent times. Capital One had an S3 bucket breached, Amazon had an unprotected Prime Video server breached, Microsoft had an Azure DevOps server breached, Puma was the victim of ransomware, etc. We had to take action. We noticed traditional cloud security tools are opaque, confusing, time consuming to set up, and expensive as you scale your cloud environment Cybersecurity vendors don't provide much actionable information to security, engineering, and devops teams by inundating them with non-contextual alerts ZeusCloud is easy to set up, transparent, and configurable, so you can prioritize the most important risks Best of all, you can use ZeusCloud for free! Future Roadmap Integrations with vulnerability scanners Integrations with secret scanners Shift-left: Remediate risks earlier in the SDLC with context from your deployments Support for Azure and GCP environments Contributing We love contributions of all sizes. What would be most helpful first: Please give us feedback in our Slack. Open a PR (see our instructions below on developing ZeusCloud locally) Submit a feature request or bug report through Github Issues. Development Run containers in development mode: cd frontend && yarn && cd -docker-compose down && docker-compose -f docker-compose.dev.yaml --env-file .env.dev up --build Reset neo4j and/or postgres data with the following: rm -rf .compose/neo4jrm -rf .compose/postgres To develop on frontend, make the the code changes and save. To develop on backend, run docker-compose -f docker-compose.dev.yaml --env-file .env.dev up --no-deps --build backend To access the UI, go to: http://localhost:80. Security Please do not run ZeusCloud exposed to the public internet. Use the latest versions of ZeusCloud to get all security related patches. Report any security vulnerabilities to founders@zeuscloud.io. Open-source vs. cloud-hosted This repo is freely available under the Apache 2.0 license. We're working on a cloud-hosted solution which handles deployment and infra management. Contact us at founders@zeuscloud.io for more information! Special thanks to the amazing Cartography project, which ZeusCloud uses for its asset inventory. Credit to PostHog and Airbyte for inspiration around public-facing materials - like this README! Download ZeusCloud

Source Cloud ZeusCloud

OWASP Global AppSec Dublin - talk and demo Features scan Amazon Route53 across an AWS Organization for domain records vulnerable to takeover scan Cloudflare for vulnerable DNS records take over vulnerable subdomains yourself before attackers and bug bounty researchers automatically create known issues in Bugcrowd or HackerOne vulnerable domains in Google Cloud DNS can be detected by Domain Protect for GCP manual scans of cloud accounts with no installation Installation the simplest way to install is to use the separate Domain Protect Deploy repository with GitHub Actions deployment workflow for other methods see Installation Collaboration We welcome collaborators! Please see the OWASP Domain Protect website for more details. Documentation Manual scans - AWS Manual scans - CloudFlare Architecture Database Reports Automated takeover optional feature Cloudflare optional feature Bugcrowd optional feature HackerOne optional feature Vulnerability types Vulnerable A records (IP addresses) optional feature Requirements Installation Slack Webhooks AWS IAM policies CI/CD Development Code Standards Automated Tests Manual Tests Conference Talks and Blog Posts Limitations This tool cannot guarantee 100% protection against subdomain takeovers. Download Domain-Protect

Cloud DNS Domain Protect OWASP Domain Protect Deploy

GCP provides an OS Login service for managing SSH access to compute instances using IAM roles. An attacker could abuse this feature via LXD, Docker (if available on the target system) and DHCP poisoning of the metadata server to escalate their privileges on a Google Compute Engine VM.

IAM roles Login GCP Login service managing SSH SSH access

Composer, Dataflow, Dataproc, Dataprep and Data Fusion all used the Compute Engine default service account by default and relied on product-level IAM permissions without requiring the iam.serviceAccount.actAs permission, meaning that users of these services could elevate their privileges. Following disclosure, GCP changed these services to require this permission.

Data Fusion Engine default multiple GCP services Composer

HWB, for a better world in cyberspace. Hackers Without Borders is an international humanitarian association that provides emergency assistance to non-governmental institutions in the event of crises and disasters related to cyberattacks.