Detection: Kubernetes Shell Running on Worker Node

Description

The following analytic identifies shell activity within the Kubernetes privilege scope on a worker node. It leverages process metrics from an OTEL collector hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization, pulled from Splunk Observability Cloud. This activity is significant as unauthorized shell processes can indicate potential security threats, providing attackers an entry point to compromise the node and the entire Kubernetes cluster. If confirmed malicious, this activity could lead to data theft, service disruption, privilege escalation, lateral movement, and further attacks, severely compromising the cluster's security and integrity.

Search

1
2| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization where `kubernetes_metrics` AND process.executable.name IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name span=10s 
3| search process.cpu.utilization>0 OR process.memory.utilization>0 
4| stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name 
5| rename host.name as host 
6| `kubernetes_shell_running_on_worker_node_filter`

Data Source

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

To implement this detection, follow these steps:

• Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
• Enable the hostmetrics/process receiver in the OTEL configuration.
• Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
• Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
• Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
• Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
• In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
• Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')
• Set the Metric Resolution to 10000.
• Leave all other settings at their default values.
• Run the Search Baseline Of Kubernetes Container Network IO Ratio

Known False Positives

unknown

Associated Analytic Story

• Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring

Risk Based Analytics (RBA)

References

• https://github.com/signalfx/splunk-otel-collector-chart/tree/main

Detection Testing

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 2