Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The following analytic identifies processes running from newly seen paths within a Kubernetes environment. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares processes observed in the last hour with those seen over the previous 30 days. This activity is significant as it may indicate unauthorized changes, compromised nodes, or the introduction of malicious software. If confirmed malicious, it could lead to unauthorized process execution, control over critical resources, data exfiltration, privilege escalation, or malware introduction within the Kubernetes cluster.
The analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information.
The analytic identifies potential scanning activities within a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes audit logs to detect multiple unauthorized access attempts (HTTP 403 responses) from the same source IP. This activity is significant as it may indicate an attacker probing for vulnerabilities or attempting to exploit known issues. If confirmed malicious, such scanning could lead to unauthorized access, data breaches, or further exploitation of the Kubernetes infrastructure, compromising the security and integrity of the environment.
The analytic detects unauthorized access attempts to Kubernetes by analyzing Kubernetes audit logs. It identifies anomalies in access patterns by examining the source of requests and their response statuses. This activity is significant for a SOC as it may indicate an attacker attempting to infiltrate the Kubernetes environment. If confirmed malicious, such access could lead to unauthorized control over Kubernetes resources, potentially compromising sensitive systems or data within the cluster.
The analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user groups. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests and user groups. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys. If confirmed malicious, this behavior could indicate an attacker attempting to exfiltrate or misuse these secrets, potentially leading to unauthorized access to sensitive systems or data.
This analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster.
The analytic identifies shell activity (sh, bash, csh, tcsh) 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.
The analytic identifies high resource utilization anomalies in Kubernetes processes. It leverages process metrics from an OTEL collector and hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The detection uses a lookup table with average and standard deviation values to spot anomalies. This activity is significant as high resource utilization can indicate security threats like cryptojacking, unauthorized data exfiltration, or compromised containers. If confirmed malicious, such anomalies can disrupt services, exhaust resources, increase costs, and allow attackers to evade detection or maintain access.
The analytic detects remote file inclusion (RFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs from the Nginx ingress controller, parsing fields such as `remote_addr`, `request`, and `url` to identify suspicious activity. This activity is significant because RFI attacks can allow attackers to execute arbitrary code or access sensitive files on the server. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the Kubernetes environment.
The following analytic detects the creation or update of a Kubernetes pod with host network attachment. It leverages Kubernetes Audit logs to identify pods configured with host network settings. This activity is significant for a SOC as it could allow an attacker to monitor all network traffic on the node, potentially capturing sensitive information and escalating privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, and service disruptions, severely impacting the security and integrity of the Kubernetes environment.
The following analytic detects anomalous changes in resource utilization ratios for processes running on a Kubernetes node. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk Observability Cloud. The detection uses a lookup table containing average and standard deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations). Significant deviations from these baselines may indicate compromised processes, malicious activity, or misconfigurations. If confirmed malicious, this could signify a security breach, allowing attackers to manipulate workloads, potentially leading to data exfiltration or service disruption.
The analytic identifies shell activity within the Kubernetes privilege scope on a worker node, specifically when shell processes are consuming CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver, pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring Add-on, focusing on process.cpu.utilization and process.memory.utilization. This activity is significant as unauthorized shell processes can indicate a security threat, potentially compromising the node and the entire Kubernetes cluster. If confirmed malicious, attackers could gain full control over the host's resources, leading to data theft, service disruption, privilege escalation, and further attacks within the cluster.
The analytic detects the pulling of known Kubernetes security scanner images such as kube-hunter, kube-bench, and kube-recon. It leverages Kubernetes logs ingested through Splunk Connect for Kubernetes, specifically monitoring for messages indicating the pulling of these images. This activity is significant because the use of security scanners can indicate an attempt to identify vulnerabilities within the Kubernetes environment. If confirmed malicious, this could lead to the discovery and exploitation of security weaknesses, potentially compromising the entire Kubernetes cluster.
The following analytic detects suspicious image pulling in Kubernetes environments. It identifies this activity by monitoring Kubernetes audit logs for image pull requests that do not match a predefined list of allowed images. This behavior is significant for a SOC as it may indicate an attacker attempting to deploy malicious software or infiltrate the system. If confirmed malicious, the impact could be severe, potentially leading to unauthorized access to sensitive systems or data, and enabling further malicious activities within the cluster.
Alerts when an authorization token originating from GKE activity is subsequently used within a command line on an endpoint system, suggesting potential credential exfiltration and reuse.
Detects when a request to the Kubernetes API is rejected due to lack of authorization or due to an expired authentication token being used. This may indicate an attacker attempting to leverage credentials they have obtained.
Detects when a request to the Kubernetes API is rejected due to lack of authorization or due to an expired authentication token being used.
This may indicate an attacker attempting to leverage credentials they have obtained.
This may indicate an attacker attempting to leverage credentials they have obtained.
Detects the creation, modification, or deletion of Kubernetes RoleBindings and ClusterRoleBindings in Google Cloud. These actions can indicate an adversary attempting to escalate privileges or maintain persistence within a Kubernetes cluster by granting themselves or other accounts additional roles and permissions.
This rule identifies the creation or modification of Kubernetes Jobs or CronJobs within a Google Cloud environment. Adversaries may use these scheduled tasks to maintain persistence, execute malicious code, or escalate privileges within a Kubernetes cluster by scheduling containers to run at specific times or intervals.
Identifies when an Azure Kubernetes CronJob or Job is created or modified in Azure Cloud. Adversaries may use Kubernetes CronJobs or Jobs for scheduling execution of malicious code that would run as a container in the cluster, establishing persistence or executing malicious tasks.
Identifies when an Azure Kubernetes Pod is deleted. This activity could indicate an attempt to disrupt services, evade detection, or remove evidence by an attacker. Monitoring these deletions is crucial for maintaining the integrity and availability of Kubernetes deployments.
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