KubeHound

KubeHound

Quick Links

• For an overview of the application architecture see the design canvas
• To see the attacks covered see the edge definitions
• To contribute a new attack to the project follow the contribution guidelines

Sample Attack Path

Contents

• Requirements
  • Application
  • Test (Development only)
• Quick Start
  • Prebuilt Releases
  • From Source
  • Sample Data
• Advanced Usage
  • Infrastructure Setup
  • Running Kubehound
• Using KubeHound Data
• Development
  • Build
  • Release build
  • Unit Testing
  • System Testing
    • Environment variable:
    • Setup
    • CI Testing
• Acknowledgements

Requirements

Application

• Golang >= 1.20: https://go.dev/doc/install
• Docker >= 19.03: https://docs.docker.com/engine/install/
• Docker Compose V2: https://docs.docker.com/compose/compose-file/compose-versioning/

Test (Development only)

• Kind: https://kind.sigs.k8s.io/docs/user/quick-start/#installing-with-a-package-manager
• Kubectl: https://kubernetes.io/docs/tasks/tools/

Quick Start

Prebuilt Releases

Release binaries are available for Linux / Windows / Mac OS via the releases page. These provide access to core KubeHound functionality but lack support for the make commands detailed in subsequent sections. Once the release archive is downloaded and extracted start the backend via:

./kubehound.sh backend-up

NOTE:

• If downloading the releases via a browser you must run e.g xattr -d com.apple.quarantine KubeHound_Darwin_arm64.tar.gz before running to prevent MacOS blocking execution

Next choose a target Kubernetes cluster, either:

• Select the targeted cluster via kubectx (need to be installed separately)
• Use a specific kubeconfig file by exporting the env variable: export KUBECONFIG=/your/path/to/.kube/config

Finally run the compiled binary with packaged configuration (config.yaml):

./kubehound.sh run

From Source

Clone this repository via git:

git clone https://github.com/DataDog/KubeHound.git

KubeHound ships with a sensible default configuration designed to get new users up and running quickly. First step is to prepare the application:

make kubehound

This will do the following:

• Start the backend services via docker compose (wiping any existing data)
• Compile the kubehound binary from source

Next choose a target Kubernetes cluster, either:

• Select the targeted cluster via kubectx (need to be installed separately)
• Use a specific kubeconfig file by exporting the env variable: export KUBECONFIG=/your/path/to/.kube/config

Finally run the compiled binary with default configuration:

bin/kubehound

To view the generated graph see the Using KubeHound Data section.

Sample Data

To view a sample graph demonstrating attacks in a very, very vulnerable cluster you can generate data via running the app against the provided kind cluster:

make sample-graph

To view the generated graph see the Using KubeHound Data section.

Advanced Usage

Infrastructure Setup

First create and populate a .env file with the required variables:

cp deployments/kubehound/.env.tpl deployments/kubehound/.env

Edit the variables (datadog env DD_* related and KUBEHOUND_ENV):

• KUBEHOUND_ENV: dev or release
• DD_API_KEY: api key you created from https://app.datadoghq.com/ website

Note:

• KUBEHOUND_ENV=dev will build the images locally (and provide some local debugging containers e.g mongo-express)
• KUBEHOUND_ENV=release will use prebuilt images from ghcr.io

Running Kubehound

To replicate the automated command and run KubeHound step-by-step. First build the application:

make build

Next spawn the backend infrastructure

make backend-up

Next create a configuration file:

collector:
  type: live-k8s-api-collector
telemetry:
  enabled: true

A tailored sample configuration file can be found here, a full configuration reference containing all possible parameters here.

Finally run the KubeHound binary, passing in the desired configuration:

bin/kubehound -c <config path>

Remember the targeted cluster must be set via kubectx or setting the KUBECONFIG environment variable. Additional functionality for managing the application can be found via:

make help

Using KubeHound Data

To query the KubeHound graph data requires using the Gremlin query language via an API call or dedicated graph query UI. A number of graph query UIs are availble, but we recommend gdotv. To access the KubeHound graph using gdotv:

• Download and install the application from https://gdotv.com/
• Create a connection to the local janusgraph instance by following the steps here https://docs.gdotv.com/connection-management/ and using hostname=localhost
• Navigate to the query editor and enter a sample query e.g g.V().count(). See detailed instructions here: https://docs.gdotv.com/query-editor/#run-your-query

Query data from your scripts

Python

You can query the database data in your python script by using the following snippet:

#!/usr/bin/env python
import sys
from gremlin_python.driver.client import Client

KH_QUERY = "kh.containers().count()"
c = Client("ws://127.0.0.1:8182/gremlin", "kh")
results = c.submit(KH_QUERY).all().result()

You'll need to install gremlinpython as a dependency via: pip install gremlinpython

Development

Build

Build the application via:

make build

All binaries will be output to the bin folder

Release build

Build the release packages locally using goreleaser:

make local-release

Unit Testing

The full suite of unit tests can be run locally via:

make test

System Testing

The repository includes a suite of system tests that will do the following:

• create a local kubernetes cluster
• collect kubernetes API data from the cluster
• run KubeHound using the file collector to create a working graph database
• query the graph database to ensure all expected vertices and edges have been created correctly

The cluster setup and running instances can be found under test/setup

If you need to manually access the system test environement with kubectl and other commands, you'll need to set (assuming you are at the root dir):

cd test/setup/ && export KUBECONFIG=$(pwd)/.kube-config

Environment variable:

• DD_API_KEY (optional): set to the datadog API key used to submit metrics and other observability data.

Setup

Setup the test kind cluster (you only need to do this once!) via:

make local-cluster-deploy

Then run the system tests via:

make system-test

To cleanup the environment you can destroy the cluster via:

make local-cluster-destroy

To list all the available commands, run:

make help

Note: if you are running on Linux but you dont want to run sudo for kind and docker command, you can overwrite this behavior by editing the following var in test/setup/.config:

• DOCKER_CMD="docker" for docker command
• KIND_CMD="kind" for kind command

CI Testing

System tests will be run in CI via the system-test github action

Acknowledgements

KubeHound was created by the Adversary Simulation Engineering (ASE) team at Datadog:

• Jeremy Fox @0xff6a
• Julien Terriac
• Edouard Schweisguth @edznux

With additional support from:

• Christophe Tafani-Dereeper @christophetd

We would also like to acknowledge the BloodHound team for pioneering the use of graph theory in offensive security and inspiring us to create this project.