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MEDIUM severity

GHSA-v7m9-9497-p9gr

MEDIUMFix: jupyterhub/kubespawner@3dfe870

GHSA-v7m9-9497-p9gr is a medium-severity (CVSS 6.8) CWE-863 vulnerability in jupyterhub-kubespawner. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-v7m9-9497-p9gr is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Possible pod name collisions in jupyterhub-kubespawner

Also known asCVE-2020-15110PYSEC-2020-51
Published
Jul 22, 2020
Updated
Jul 8, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

1 pkg affected
🐍jupyterhub-kubespawner

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

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

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIjupyterhub-kubespawnerall versions0.12.0
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for jupyterhub-kubespawner. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update jupyterhub-kubespawner to 0.12.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-v7m9-9497-p9gr is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-v7m9-9497-p9gr is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-v7m9-9497-p9gr. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### 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 serv
O3 Security · Impact-Aware SCA

Is GHSA-v7m9-9497-p9gr in your dependencies?

O3 detects GHSA-v7m9-9497-p9gr across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

GHSA-v7m9-9497-p9gr: Possible pod name… | O3 Security