| ID | Technique | Tactic |
|---|---|---|
| T1526 | Cloud Service Discovery | Discovery |
Detection: Kubernetes Suspicious Image Pulling
Description
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.
Search
1`kube_audit` requestObject.message="Pulling image*"
2| search NOT `kube_allowed_images`
3| fillnull
4| stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb
5| rename sourceIPs{} as src_ip, user.username as user
6| `kubernetes_suspicious_image_pulling_filter`
Data Source
| Name | Platform | Sourcetype | Source | Supported App |
|---|---|---|---|---|
| Kubernetes Audit |  Kubernetes |
'_json' |
'kubernetes' |
N/A |
Macros Used
| Name | Value |
|---|---|
| kube_allowed_images | objectRef.name IN (*splunk*, *falco*) |
| kubernetes_suspicious_image_pulling_filter | search * |
kubernetes_suspicious_image_pulling_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_13
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Risk Event | True |
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.
Implementation
The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
Known False Positives
unknown
Associated Analytic Story
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| Suspicious image $objectRef.name$ pulled in Kubernetes from ip $src_ip$ by user $user$ | 49 | 70 | 70 |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | kubernetes |
_json |
| Integration | ✅ Passing | Dataset | kubernetes |
_json |
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