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splunk_escuAnomaly
Kubernetes Shell Running on Worker Node
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.
MITRE ATT&CK
Detection Query
| 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
| search process.cpu.utilization>0 OR process.memory.utilization>0
| 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
| rename host.name as host
| `kubernetes_shell_running_on_worker_node_filter`Author
Matthew Moore, Splunk
Created
2026-03-10
Tags
Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring
Raw Content
name: Kubernetes Shell Running on Worker Node
id: efebf0c4-dcf4-496f-85a2-5ab7ad8fa876
version: 9
date: '2026-03-10'
author: Matthew Moore, Splunk
status: experimental
type: Anomaly
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.
data_source: []
search: |-
| 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
| search process.cpu.utilization>0 OR process.memory.utilization>0
| 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
| rename host.name as host
| `kubernetes_shell_running_on_worker_node_filter`
how_to_implement: "To implement this detection, follow these steps:\n* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.\n* 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')\n* Set the Metric Resolution to 10000.\n * Leave all other settings at their default values.\n* Run the Search Baseline Of Kubernetes Container Network IO Ratio"
known_false_positives: No false positives have been identified at this time.
references:
- https://github.com/signalfx/splunk-otel-collector-chart/tree/main
rba:
message: Kubernetes shell running on worker node on host $host$
risk_objects:
- field: host
type: system
score: 20
threat_objects: []
tags:
analytic_story:
- Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring
asset_type: Kubernetes
mitre_attack_id:
- T1204
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
security_domain: network