The following analytic detects instances where a shell is spawned within a Kubernetes container, a behavior often indicative of an attacker gaining unauthorized access. Leveraging Falco, a cloud-native runtime security tool, this analytic monitors system calls within the Kubernetes environment, flagging when a shell is spawned in a container. This behavior is worth identifying for a SOC as it could potentially allow an attacker to execute arbitrary commands, manipulate container processes, or escalate privileges, posing a significant threat to the integrity and security of the Kubernetes infrastructure. The impact of such an attack could be severe, leading to data breaches, service disruptions, or unauthorized access to sensitive information.
- Type: Anomaly
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-12-13
- Author: Patrick Bareiss, Splunk
- ID: d2feef92-d54a-4a19-8306-b47c6ceba5b2
Kill Chain Phase
- CIS 13
`kube_container_falco` "A shell was spawned in a container"
| stats count by container_image container_image_tag container_name parent proc_exepath process user
The SPL above uses the following Macros:
kubernetes_falco_shell_spawned_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
The detection is based on data that originates from Falco, a cloud native runtime security tool. Falco is designed to detect anomalous activity in your applications and is a crucial component of this detection rule. To implement this detection rule, you need to install and configure Falco in your Kubernetes environment. Once Falco is set up, it will monitor the system calls in your Kubernetes infrastructure and generate logs for any suspicious activity. These logs are then ingested by Splunk for analysis. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs.
Known False Positives
Associated Analytic Story
|A shell is spawned in the container $container_name$ by user $user$.
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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