GHSA-v9g2-g7j4-4jxc
MEDIUMjupyter-scheduler's endpoint is missing authentication
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
jupyter-scheduler🐍jupyter-scheduler🐍jupyter-scheduler🐍jupyter-schedulerReal-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
jupyter_scheduler is missing an authentication check in Jupyter Server on an API endpoint (GET /scheduler/runtime_environments) which lists the names of the Conda environments on the server. In affected versions, jupyter_scheduler allows an unauthenticated user to obtain the list of Conda environment names on the server. This reveals any information that may be present in a Conda environment name.
This issue does not allow an unauthenticated third party to read, modify, or enter the Conda environments present on the server where jupyter_scheduler is running. This issue only reveals the list of Conda environment names.
Impacted versions: >=1.0.0,<=1.1.5 ; ==1.2.0 ; >=1.3.0,<=1.8.1 ; >=2.0.0,<=2.5.1
Patches
jupyter-scheduler==1.1.6jupyter-scheduler==1.2.1jupyter-scheduler==1.8.2jupyter-scheduler==2.5.2
Workarounds
Server operators who are unable to upgrade can disable the jupyter-scheduler extension with:
jupyter server extension disable jupyter-scheduler
References
If you have any questions or comments about this advisory we ask that you contact AWS/Amazon Security via our vulnerability reporting page [1] or directly via email to [email protected]. Please do not create a public GitHub issue.
[1] Vulnerability reporting page: https://aws.amazon.com/security/vulnerability-reporting
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | jupyter-scheduler | ≥ 1.0.0&&< 1.1.6 | 1.1.6 |
| 🐍PyPI | jupyter-scheduler | ≥ 1.2.0&&< 1.2.1 | 1.2.1 |
| 🐍PyPI | jupyter-scheduler | ≥ 1.3.0&&< 1.8.2 | 1.8.2 |
| 🐍PyPI | jupyter-scheduler | ≥ 2.0.0&&< 2.5.2 | 2.5.2 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for jupyter-scheduler. 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.
Fix
Update jupyter-scheduler to 1.1.6 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-v9g2-g7j4-4jxc is resolved across your whole dependency graph.
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.
How O3 protects you
O3 pinpoints whether GHSA-v9g2-g7j4-4jxc 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-v9g2-g7j4-4jxc. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-v9g2-g7j4-4jxc in your dependencies?
O3 detects GHSA-v9g2-g7j4-4jxc across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.