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Rancher vulnerable to command injection through unsanitized YAML parameter

Critical severity GitHub Reviewed Published May 27, 2026 in rancher/rancher

Package

gomod github.com/rancher/rancher (Go)

Affected versions

>= 2.14.0, < 2.14.2
>= 2.13.0, < 2.13.6
>= 2.12.0, < 2.12.10
>= 2.11.0, < 2.11.14
>= 2.10.0, < 2.10.12
< 0.0.0-20260617231817-2aa77eb283e7

Patched versions

2.14.2
2.13.6
2.12.10
2.11.14
2.10.12
0.0.0-20260617231817-2aa77eb283e7

Description

Impact

A critical command injection vulnerability has been identified in the Rancher Manager cluster import endpoint /v3/import/{token}_{clusterId}.yaml through unsanitized YAML parameters. This endpoint accepts an authImage query parameter that is rendered without sanitization into a generated Kubernetes manifest template. By including URL-encoded newlines in the parameter value, an attacker can break out of the image: field to inject arbitrary YAML keys and malicious configurations, such as commands to execute malicious containers.

Exploitation of this vulnerability requires the following conditions to be met:

  • Attackers must obtain a valid cluster registration token (these tokens may be exposed, for example, through documentation, screenshots, or insecure communication channels).
  • The victim’s cluster operator must execute kubectl apply against a maliciously crafted URL.

When a victim applies this compromised manifest using kubectl apply, a DaemonSet is deployed with the injected configuration. This DaemonSet:

  • Runs on all control-plane nodes with hostNetwork: true enabled.
  • Uses the cattle service account, which possesses cluster-admin privileges.
  • Mounts /etc/kubernetes directly from the host.
  • Executes attacker-controlled commands via the injected command: field.

An attacker who successfully exploits this vulnerability could:

  • Achieve full control over downstream Kubernetes clusters.
  • Execute arbitrary code on control-plane nodes with elevated privileges.
  • Access sensitive cluster secrets and configurations via the privileged service account.
  • Disrupt cluster operations by manipulating critical control-plane workloads.
  • Establish persistent access through the deployed DaemonSet.

Note: If you believe that you might have been impacted by this vulnerability, it's highly advised to review your clusters' logs and deployment logs for signs of malicious deployments and to rotate all service accounts and credentials that might have been exposed in such a scenario.

Please refer to the associated MITRE ATT&CK - Technique - Deploy Container for further information about this category of attack.

Patches

This vulnerability is addressed by validating the authImage parameter to ensure it contains only valid OCI image reference characters, rejecting any input containing newlines, whitespace, or other characters that could break YAML syntax.

Patched versions of Rancher include release v2.14.2, v2.13.6, v2.12.10, v2.11.14 and v2.10.12.

Workarounds

If upgrading to a patched version immediately is not feasible, users are encouraged to apply the following workaround:

  • Review the kube-api-auth DaemonSet: Inspect downstream clusters for the kube-api-auth DaemonSet within the cattle-system namespace (which targets control-plane nodes). Review this resource configuration carefully for:
    • Unexpected command: or args: fields in the container specification.
    • References to non-standard or suspicious container images.
    • Any modifications occurring after the initial cluster import.
  • Validate manifest integrity: Before running kubectl apply on any import manifests, verify that the source URLs originate from trusted sources and match expected patterns.

Credits

This security issue was reported by the following collaborators according to our responsible disclosure policy:

  • Radisauskas Arnoldas from NATO and the NATO Cyber Security Centre (NCSC).
  • Michael Wollner from Deutsche Telekom AG.

References

If you have any questions or comments about this advisory:

References

@samjustus samjustus published to rancher/rancher May 27, 2026
Published by the National Vulnerability Database Jun 19, 2026
Published to the GitHub Advisory Database Jul 1, 2026
Reviewed Jul 1, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability High

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(67th percentile)

Weaknesses

Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')

The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. eval). Learn more on MITRE.

CVE ID

CVE-2026-44939

GHSA ID

GHSA-mhc6-2gfq-xx62

Source code

Credits

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