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Description

This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2020-07-11
  • Author: Rod Soto, Splunk
  • ID: a5bed417-070a-41f2-a1e4-82b6aa281557

Annotations

ATT&CK
Kill Chain Phase
  • Exploitation
NIST
CIS20
CVE
1
2
3
4
`google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous 
| table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path 
|dedup src_ip src_user 
|`kubernetes_gcp_detect_suspicious_kubectl_calls_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_gcp_detect_suspicious_kubectl_calls_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required field

  • _time

How To Implement

You must install splunk add on for GCP. This search works with pubsub messaging logs.

Known False Positives

Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets

Associated Analytic story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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

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