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Possible pod name collisions in jupyterhub-kubespawner

High severity GitHub Reviewed Published Jul 17, 2020 in jupyterhub/kubespawner • Updated Sep 24, 2024

Package

pip jupyterhub-kubespawner (pip)

Affected versions

<= 0.11.1

Patched versions

0.12.0

Description

Impact

What kind of vulnerability is it? Who is impacted?

JupyterHub deployments using:

  • KubeSpawner <= 0.11.1 (e.g. zero-to-jupyterhub 0.9.0) and
  • enabled named_servers (not default), and
  • an Authenticator that allows:
    • usernames with hyphens or other characters that require escape (e.g. user-hyphen or user@email), and
    • usernames which may match other usernames up to but not including the escaped character (e.g. user in the above cases)

In this circumstance, certain usernames will be able to craft particular server names which will grant them access to the default server of other users who have matching usernames.

Patches

Has the problem been patched? What versions should users upgrade to?

Patch will be released in kubespawner 0.12 and zero-to-jupyterhub 0.9.1

Workarounds

Is there a way for users to fix or remediate the vulnerability without upgrading?

KubeSpawner

Specify configuration:

for KubeSpawner

from traitlets import default
from kubespawner import KubeSpawner

class PatchedKubeSpawner(KubeSpawner):
    @default("pod_name_template")
    def _default_pod_name_template(self):
        if self.name:
            return "jupyter-{username}-{servername}"
        else:
            return "jupyter-{username}"

    @default("pvc_name_template")
    def _default_pvc_name_template(self):
        if self.name:
            return "claim-{username}-{servername}"
        else:
            return "claim-{username}"

c.JupyterHub.spawner_class = PatchedKubeSpawner

Note for KubeSpawner: this configuration will behave differently before and after the upgrade, so will need to be removed when upgrading. Only apply this configuration while still using KubeSpawner ≤ 0.11.1 and remove it after upgrade to ensure consistent pod and pvc naming.

Changing the name template means pvcs for named servers will have different names. This will result in orphaned PVCs for named servers across Hub upgrade! This may appear as data loss for users, depending on configuration, but the orphaned PVCs will still be around and data can be migrated manually (or new PVCs created manually to reference existing PVs) before deleting the old PVCs and/or PVs.

References

Are there any links users can visit to find out more?

For more information

If you have any questions or comments about this advisory:

Credit: Jining Huang

References

@minrk minrk published to jupyterhub/kubespawner Jul 17, 2020
Reviewed Jul 17, 2020
Published by the National Vulnerability Database Jul 17, 2020
Published to the GitHub Advisory Database Jul 22, 2020
Last updated Sep 24, 2024

Severity

High

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 Present
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

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:P/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N

EPSS score

0.153%
(52nd percentile)

Weaknesses

CVE ID

CVE-2020-15110

GHSA ID

GHSA-v7m9-9497-p9gr

Source code

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