Queries

You can query KubeHound data stored in the JanusGraph database by using the Gremlin query language.

Basic queries

Count the number of pods in the cluster

g.V().hasLabel("Pod").count()

View all possible container escapes in the cluster

g.V().hasLabel("Container").outE().inV().hasLabel("Node").path()

List the names of all possible attacks in the cluster

g.E().groupCount().by(label)

View all the mounted host path volumes in the cluster

g.V().hasLabel("Volume").has("type", "HostPath").groupCount().by("sourcePath")

View host path mounts that can be exploited to escape to a node

g.E().hasLabel("EXPLOIT_HOST_READ", "EXPLOIT_HOST_WRITE").outV().groupCount().by("sourcePath")

View all service endpoints by service name in the cluster

// Leveraging the "EndpointExposureType" enum value to filter only on services
// c.f. https://github.com/DataDog/KubeHound/blob/main/pkg/kubehound/models/shared/constants.go
g.V().hasLabel("Endpoint").has("exposure", 3).groupCount().by("serviceEndpoint")

Basic attack paths

All paths between an endpoint and a node

g.V().hasLabel("Endpoint").repeat(out().simplePath()).until(hasLabel("Node")).path()

All paths (up to 5 hops) between a container and a node

g.V().hasLabel("Container").repeat(out().simplePath()).until(hasLabel("Node").or().loops().is(5)).hasLabel("Node").path()

All attack paths (up to 6 hops) from any compomised identity (e.g. service account) to a critical asset

g.V().hasLabel("Identity").repeat(out().simplePath()).until(has("critical", true).or().loops().is(6)).has("critical", true).path().limit(5)

Attack paths from compromised assets

Containers

Attack paths (up to 10 hops) from a known breached container to any critical asset

g.V().hasLabel("Container").has("name", "nsenter-pod").repeat(out().simplePath()).until(has("critical", true).or().loops().is(10)).has("critical", true).path()

Attack paths (up to 10 hops) from a known backdoored container image to any critical asset

g.V().hasLabel("Container").has("image", TextP.containing("malicious-image")).repeat(out().simplePath()).until(has("critical", true).or().loops().is(10)).has("critical", true).path()

Credentials

Attack paths (up to 10 hops) from a known breached identity to a critical asset

g.V().hasLabel("Identity").has("name", "compromised-sa").repeat(out().simplePath()).until(has("critical", true).or().loops().is(10)).has("critical", true).path()

Endpoints

Attack paths (up to 6 hops) from any endpoint to a critical asset:

g.V().hasLabel("Endpoint").repeat(out().simplePath()).until(has("critical", true).or().loops().is(6)).has("critical", true).path().limit(5)

Attack paths (up to 10 hops) from a known risky endpoint (e.g JMX) to a critical asset

g.V().hasLabel("Endpoint").has("portName", "jmx").repeat(out().simplePath()).until(has("critical", true).or().loops().is(6)).has("critical", true).path().limit(5)

Risk assessment

What is the shortest exploitable path between an exposed service and a critical asset?

g.V().hasLabel("Endpoint").has("exposure", gte(3)).repeat(out().simplePath()).until(has("critical", true).or().loops().is(7)).has("critical", true).path().count(local).min()

What percentage of external facing services have an exploitable path to a critical asset?

// Leveraging the "EndpointExposureType" enum value to filter only on services
// c.f. https://github.com/DataDog/KubeHound/blob/main/pkg/kubehound/models/shared/constants.go

// Base case
g.V().hasLabel("Endpoint").has("exposure", gte(3)).count()

// Has a critical path
g.V().hasLabel("Endpoint").has("exposure", gte(3)).where(repeat(out().simplePath()).until(has("critical", true).or().loops().is(10)).has("critical", true).limit(1)).count()

CVE impact assessment

You can also use KubeHound to determine if workloads in your cluster may be vulnerable to a specific vulnerability.

First, evaluate if a known vulnerable image is running in the cluster:

g.V().hasLabel("Container").has("image", TextP.containing("elasticsearch")).groupCount().by("image")

Then, check any exposed services that could be affected and have a path to a critical asset. This helps prioritizing patching and remediation.

g.V().hasLabel("Container").has("image", "dockerhub.com/elasticsearch:7.1.4").where(inE("ENDPOINT_EXPLOIT").outV().has("exposure", gte(3))).where(repeat(out().simplePath()).until(has("critical", true).or().loops().is(10)).has("critical", true).limit(1))

Assessing the value of implementing new security controls

To verify concrete impact, this can be achieved by comparing the difference in the key risk metrics above, before and after the control change. To simulate the impact of introducing a control (e.g to evaluate ROI), we can add conditions to our path queries. For example if we wanted to evaluate the impact of adding a gatekeeper rule that would deny the use of hostPID we can use the following:

What percentage level of attack path reduction was achieved by the introduction of a control?

// Calculate the base case
g.V().hasLabel("Endpoint").has("exposure", gte(3)).repeat(out().simplePath()).until(has("critical", true).or().loops().is(6)).has("critical", true).path().count()

// Calculate the impact of preventing CE_NSENTER attack
g.V().hasLabel("Endpoint").has("exposure", gte(3)).repeat(outE().not(hasLabel("CE_NSENTER")).inV().simplePath()).emit().until(has("critical", true).or().loops().is(6)).has("critical", true).path().count()

What type of control would cut off the largest number of attack paths to a specific asset in the cluster?

// We count the number of instances of unique attack paths using
g.V().hasLabel("Container").repeat(outE().inV().simplePath()).emit()
.until(has("critical", true).or().loops().is(6)).has("critical", true)
.path().by(label).groupCount().order(local).by(select(values), desc)

/* Sample output:

  {
    "path[Container, IDENTITY_ASSUME, Identity, PERMISSION_DISCOVER, PermissionSet, TOKEN_LIST, Identity, PERMISSION_DISCOVER, PermissionSet, TOKEN_LIST, Identity, PERMISSION_DISCOVER, PermissionSet]" : 191,
    "path[Container, CE_SYS_PTRACE, Node, VOLUME_EXPOSE, Volume, TOKEN_STEAL, Identity, PERMISSION_DISCOVER, PermissionSet, TOKEN_LIST, Identity, PERMISSION_DISCOVER, PermissionSet]" : 48,
    "path[Container, IDENTITY_ASSUME, Identity, PERMISSION_DISCOVER, PermissionSet, TOKEN_BRUTEFORCE, Identity, PERMISSION_DISCOVER, PermissionSet, TOKEN_LIST, Identity, PERMISSION_DISCOVER, PermissionSet]" : 48,
    ...
  }
*/

Threat modelling

All unique attack paths by labels to a specific asset (here, the cluster-admin role)

g.V().hasLabel("Container", "Identity")
.repeat(out().simplePath())
.until(has("name", "cluster-admin").or().loops().is(5))
.has("name", "cluster-admin").hasLabel("Role").path().as("p").by(label).dedup().select("p").path()

All unique attack paths by labels to a any critical asset

g.V().hasLabel("Container", "Identity")
.repeat(out().simplePath())
.until(has("critical", true).or().loops().is(5))
.has("critical", true).path().as("p").by(label).dedup().select("p").path()

Tips for writing queries

To get started with Gremlin, have a look at the following tutorials:

• Gremlin basics by Daniel Kuppitz
• Gremlin advanced by Daniel Kuppitz

For large clusters it is recommended to add a limit() step to all queries where the graph output will be examined in the UI to prevent overloading it. An example looking for attack paths possible from a sample of 5 containers would look like:

g.V().hasLabel("Container").limit(5).outE()

Additional tips: - For queries to be displayed in the UI, try to limit the output to 1000 elements or less - Enable large cluster optimizations via configuration file if queries are returning too slowly - Try to filter the initial element of queries by namespace/service/app to avoid generating too many results, for instance g.V().hasLabel("Container").has("namespace", "your-namespace")