Quick answer
Direct answer
Risk scores
| CVSS metric | What it means |
|---|---|
| Attack vector (AV:N) | Network: reachable remotely |
| Attack complexity (AC:L) | Low complexity |
| Privileges required (PR:N) | No privileges required |
| User interaction (UI:R) | A user must take an action |
| Scope (S:U) | Scope unchanged |
| Confidentiality (C:H) | High confidentiality impact |
| Integrity (I:H) | High integrity impact |
| Availability (A:H) | High availability impact |
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
About Jupyter Server
CVE-2026-6657 affects Jupyter Server, a product from Jupyter. Before acting, confirm where Jupyter Server runs in your estate, who owns it and whether it is reachable from the internet, because exposure decides real risk more than the score alone.
What the flaw is
In plain terms: A vulnerability in jupyter-server versions 1.12.0 through 2.17.0 allows an attacker to bypass CORS origin validation when the `allow_origin_pat` configuration is used. The issue arises from the use of `re.match()` for validating the `Origin` header, which only anchors at the start of the string. This allows attacker-controlled domains such as `trusted.example.com.evil.com` to pass validation against patterns intended to match `trusted.example.com`. The vulnerability affects multiple locations in the codebase, including CORS headers, WebSocket connections, referer validation, and login redirects, potentially enabling phishing attacks, arbitrary code execution, and unauthorized access to sensitive API responses.
Based on its CVSS vector, the flaw can be reached over the network, needs no login, and relies on a user taking an action. Apply the vendor fix and confirm exposure on internet facing and business critical assets first.
Affected versions and timeline
| Field | Detail |
|---|---|
| Product | Jupyter Server |
| Affected versions | Jupyter Server from 1.12.0 up to and including 2.17.0 |
| Published | Jun 3, 2026 |
| Last modified | Jul 21, 2026 |
| Added to CISA KEV | Not listed |
| Assigning CNA | security@huntr.dev |
How to detect it
- Inventory every instance of Jupyter Server and record the running version against the affected range above.
- Check external attack surface scans for internet facing instances of the component.
- Review logs on affected systems for unusual requests, errors or new processes around the vulnerable function.
- Confirm with an authenticated vulnerability scan once your scanner has a check for this CVE.
How to fix it
- Apply the fixed version of Jupyter Server named in the vendor advisory.
- If patching must wait, restrict network access to the affected service and put it behind authentication or a web application firewall rule.
- Rescan after the change to prove the exposure is gone, then close the ticket.
CTEM action plan
| CTEM stage | What to do for this CVE |
|---|---|
| Scope | Add every business service that depends on Jupyter Server to the current CTEM cycle. |
| Discover | Find all instances, including shadow and cloud deployments, and record versions and exposure. |
| Prioritize | Rank using EPSS and CVSS together with asset exposure and business impact. |
| Validate | Confirm the flaw is reachable and exploitable in your environment and that compensating controls work. |
| Mobilize | Assign the fix to the asset owner with a due date matched to the priority, then verify closure. |
CWE References
Technical References
Frequently asked questions
Related CVEs
Jupyter Enterprise Gateway launches remote Jupyter Notebook kernels across distributed clusters like Apache Spark, Kubernetes, and Docker Swarm. In versions prior to 3.3.0, the server interpolates untrusted environment variables (e.g., KERNEL_XXX) into Kubernetes manifests without YAML-aware escaping, enabling YAML injection attacks. Attackers can inject new fields, overwrite critical fields (e.g., duplicate securityContext keys, where the last one prevails), and inject document boundaries (--- for new documents, ... for end-of-document) to generate multiple resources, potentially creating arbitrary types, such as privileged pods. The Jinja2 template for the Kubernetes manifest contains several kernel_xxx variables, such as kernel_working_dir that are used when rendering the manifest and are all vectors for YAML injection. This issue has been fixed in version 3.3.0.
Jupyter Enterprise Gateway launches remote Jupyter Notebook kernels across distributed clusters like Apache Spark, Kubernetes, and Docker Swarm. In versions 2.0.0rc2 and above, prior to 3.3.0, the environment variables (KERNEL_XXX) used during the rendering of the Kubernetes manifest are vulnerable to Server Side Template Injection (SSTI). By including Jinja2 template expressions it is possible to execution Python code and OS Commands in the Enterprise Gateway service. The code can use or steal the Kubernetes service account token, which can steal Kubernetes secrets and be used to fully compromise the Kubernetes cluster by scheduling a privileged pod or a pod with a hostPath volume mount. This issue has been fixed in version 3.3.0.
JupyterLab Git is a Git extension for JupyterLab. From 0.30.0b3 before 0.54.0, the PlainTextDiff.ts createHeader() method passes Git filenames directly to innerHTML when rendering renamed files in commit history, allowing a crafted filename to execute JavaScript when a victim views the rename diff in the Git History tab. This issue is fixed in version 0.54.0.
A path traversal vulnerability exists in jupyter-server version 2.17.0 due to an incorrect root directory boundary check in the _get_os_path() function within jupyter_server/services/contents/fileio.py. The check uses startswith(root) without appending a trailing path separator, allowing sibling directories with names starting with the same prefix as root_dir to bypass the check. Additionally, the to_os_path() function in utils.py does not strip ".." from path parts, enabling traversal sequences to bypass the vulnerable check. This vulnerability can lead to unauthorized read/write access to files in sibling directories, potentially exposing sensitive data in shared hosting environments.
jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. Prior to 4.5.7, JupyterLab's HTML sanitizer allowlists data-commandlinker-command and data-commandlinker-args on button elements, while CommandLinker listens for all click events on document.body and executes the named command without checking whether the element came from trusted JupyterLab UI. A notebook with a pre-saved HTML cell output containing a deceptive button can trigger arbitrary JupyterLab commands - including arbitrary code execution - on a single user click, without any code being submitted for execution by the user. This vulnerability is fixed in 4.5.7.
Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the Origin header validation uses Python's re.match() to check incoming origins against the allow_origin_pat configuration value. Because re.match() only anchors at the start of the string and does not require a full match, a pattern intended to match only a trusted domain (e.g., trusted.example.com) will also match any origin that begins with that domain followed by additional characters (e.g., trusted.example.com.evil.com). An attacker who controls such a domain can bypass the CORS origin restriction and make cross-origin requests to the Jupyter Server API from an untrusted site. This issue has been fixed in version 2.18.0.
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