CVE-2026-40110: Jupyter CORS bypass — HIGH

Q: Is CVE-2026-40110 actively exploited?

No confirmed active exploitation of CVE-2026-40110 has been reported, but organizations should still patch proactively.

Q: How to fix CVE-2026-40110?

1. Patch: Upgrade jupyter-server to 2.18.0 (commits 057869a and 49b3439 resolve the issue). 2. Workaround: Immediately audit all allow_origin_pat configuration values and wrap each pattern with ^ and $ anchors (e.g., change trusted\.example\.com to ^trusted\.example\.com$). 3. Network controls: Restrict Jupyter Server access to localhost or trusted internal subnets; never expose Jupyter directly to the internet. 4. Audit dependents: Check whether downstream packages (JupyterLab, JupyterHub, notebook) in your environment pull jupyter-server <= 2.17.0 and upgrade those dependency chains. 5. Detection: Monitor web proxy and WAF logs for cross-origin requests to Jupyter endpoints (/api/kernels, /api/sessions) from unexpected origins.

Q: What systems are affected by CVE-2026-40110?

This vulnerability affects the following AI/ML architecture patterns: ML development environments, Training pipelines using notebook orchestration, JupyterHub multi-user deployments, MLOps pipelines with programmatic notebook execution, Data science workstations with browser-accessible Jupyter.

Q: What is the CVSS score for CVE-2026-40110?

CVE-2026-40110 has a CVSS v3.1 base score of 7.1 (HIGH). The EPSS exploitation probability is 0.34%.

CISO Take

Jupyter Server's Origin header validation uses re.match() instead of re.fullmatch(), allowing any attacker who controls a domain starting with the trusted pattern (e.g., trusted.example.com.evil.com) to bypass CORS protections entirely. With 1,862 downstream dependents and Jupyter's near-universal presence in AI/ML development environments, this creates a wide cross-origin attack surface where a malicious web page can reach the Jupyter kernel API and execute arbitrary code against any unpatched instance — the EPSS places this in the top 87th percentile for exploitation likelihood despite low absolute probability. Patch to jupyter-server 2.18.0 immediately; if patching is not possible, anchor all allow_origin_pat values with ^ and $ as an interim workaround.

Sources: NVD GitHub Advisory EPSS OpenSSF ATLAS

What is the risk?

HIGH risk for organizations where Jupyter Server is deployed on internal networks or developer workstations accessible from a browser. The vulnerability is straightforward to weaponize — an attacker needs only to register a domain that begins with the trusted prefix and serve a malicious page. Jupyter's kernel API permits arbitrary Python execution, so a successful CORS bypass grants effectively full code execution on the server with the process owner's privileges, making this a high-impact stepping stone into AI/ML development pipelines.

How does the attack unfold?

Infrastructure Setup

Attacker registers a domain crafted to match the victim's allow_origin_pat regex prefix (e.g., trusted.example.com.evil.com), exploiting re.match()'s lack of end anchoring.

AML.T0008.002

CORS Validation Bypass

Victim developer visits attacker-controlled page; browser sends the crafted Origin header which passes Jupyter Server's unanchored regex check and receives permissive CORS headers in response.

AML.T0049

Kernel API Exploitation

Attacker's JavaScript enumerates active kernels and posts execute requests to Jupyter's kernel API, achieving arbitrary Python code execution under the server process's privileges.

AML.T0049

AI Artifact Exfiltration

Executed code reads notebooks, model weights, embedded cloud credentials, and training datasets from the development environment and exfiltrates them to attacker infrastructure.

AML.T0035

Infrastructure Setup

Attacker registers a domain crafted to match the victim's allow_origin_pat regex prefix (e.g., trusted.example.com.evil.com), exploiting re.match()'s lack of end anchoring.

AML.T0008.002

CORS Validation Bypass

Victim developer visits attacker-controlled page; browser sends the crafted Origin header which passes Jupyter Server's unanchored regex check and receives permissive CORS headers in response.

AML.T0049

Kernel API Exploitation

Attacker's JavaScript enumerates active kernels and posts execute requests to Jupyter's kernel API, achieving arbitrary Python code execution under the server process's privileges.

AML.T0049

AI Artifact Exfiltration

Executed code reads notebooks, model weights, embedded cloud credentials, and training datasets from the development environment and exfiltrates them to attacker infrastructure.

AML.T0035

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
Jupyter	pip	<= 2.17.0	`2.18.0`
13.3K OpenSSF 5.8 1.9K dependents Pushed yesterday 56% patched ~28d to patch Full package profile →

Do you use Jupyter? You're affected.

How severe is it?

CVSS 3.1

7.1 / 10

EPSS

0.3%

chance of exploitation in 30 days

Higher than 27% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Moderate

What is the attack surface?

AV Network

AC Low

PR None

UI Required

S Unchanged

C High

I Low

A None

What should I do?

5 steps

Patch: Upgrade jupyter-server to 2.18.0 (commits 057869a and 49b3439 resolve the issue).
Workaround: Immediately audit all allow_origin_pat configuration values and wrap each pattern with ^ and $ anchors (e.g., change trusted\.example\.com to ^trusted\.example\.com$).
Network controls: Restrict Jupyter Server access to localhost or trusted internal subnets; never expose Jupyter directly to the internet.
Audit dependents: Check whether downstream packages (JupyterLab, JupyterHub, notebook) in your environment pull jupyter-server <= 2.17.0 and upgrade those dependency chains.
Detection: Monitor web proxy and WAF logs for cross-origin requests to Jupyter endpoints (/api/kernels, /api/sessions) from unexpected origins.

What does CISA's SSVC say?

Decision Track

Exploitation none

Automatable No

Technical Impact total

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

Auth Bypass Code Execution Data Extraction Framework API AML.T0008.002 - Domains AML.T0025 - Exfiltration via Cyber Means AML.T0035 - AI Artifact Collection AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.6 - Security of AI system development environments

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain the value of deployed AI systems are evaluated and improved

OWASP LLM Top 10

LLM05:2025 - Supply Chain Vulnerabilities

Frequently Asked Questions

What is CVE-2026-40110?

Jupyter Server's Origin header validation uses re.match() instead of re.fullmatch(), allowing any attacker who controls a domain starting with the trusted pattern (e.g., trusted.example.com.evil.com) to bypass CORS protections entirely. With 1,862 downstream dependents and Jupyter's near-universal presence in AI/ML development environments, this creates a wide cross-origin attack surface where a malicious web page can reach the Jupyter kernel API and execute arbitrary code against any unpatched instance — the EPSS places this in the top 87th percentile for exploitation likelihood despite low absolute probability. Patch to jupyter-server 2.18.0 immediately; if patching is not possible, anchor all allow_origin_pat values with ^ and $ as an interim workaround.

Is CVE-2026-40110 actively exploited?

No confirmed active exploitation of CVE-2026-40110 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-40110?

1. Patch: Upgrade jupyter-server to 2.18.0 (commits 057869a and 49b3439 resolve the issue). 2. Workaround: Immediately audit all allow_origin_pat configuration values and wrap each pattern with ^ and $ anchors (e.g., change trusted\.example\.com to ^trusted\.example\.com$). 3. Network controls: Restrict Jupyter Server access to localhost or trusted internal subnets; never expose Jupyter directly to the internet. 4. Audit dependents: Check whether downstream packages (JupyterLab, JupyterHub, notebook) in your environment pull jupyter-server <= 2.17.0 and upgrade those dependency chains. 5. Detection: Monitor web proxy and WAF logs for cross-origin requests to Jupyter endpoints (/api/kernels, /api/sessions) from unexpected origins.

What systems are affected by CVE-2026-40110?

This vulnerability affects the following AI/ML architecture patterns: ML development environments, Training pipelines using notebook orchestration, JupyterHub multi-user deployments, MLOps pipelines with programmatic notebook execution, Data science workstations with browser-accessible Jupyter.

What is the CVSS score for CVE-2026-40110?

CVE-2026-40110 has a CVSS v3.1 base score of 7.1 (HIGH). The EPSS exploitation probability is 0.34%.

What is the AI security impact?

Affected AI Architectures

ML development environmentsTraining pipelines using notebook orchestrationJupyterHub multi-user deploymentsMLOps pipelines with programmatic notebook executionData science workstations with browser-accessible Jupyter

MITRE ATLAS Techniques

AML.T0008.002 Domains

AML.T0025 Exfiltration via Cyber Means

AML.T0035 AI Artifact Collection

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.6.2.6

NIST AI RMF: MANAGE-2.2

OWASP LLM Top 10: LLM05:2025

What are the technical details?

Original Advisory

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.

Exploitation Scenario

An adversary targeting an AI development team registers trusted.example.com.evil.com — a domain crafted to match the victim's allow_origin_pat regex at the start while appending an attacker-controlled suffix. They deploy a malicious page on this domain with JavaScript that fetches the list of active Jupyter kernels (GET /api/kernels), selects one, and posts an execute request (POST /api/kernels/<id>/execute) containing Python code to read notebook files, harvest embedded cloud API keys or model registry tokens, and exfiltrate them to the attacker's infrastructure. A targeted phishing email lures a developer to visit the malicious page; since the browser sends the crafted Origin header and Jupyter's re.match() check passes, CORS headers are returned, the browser lifts the same-origin restriction, and the JavaScript silently exfiltrates the environment's AI artifacts without any user interaction beyond the initial page load.

Weaknesses (CWE)

CWE-777 Regular Expression without Anchors Primary CWE-777 Regular Expression without Anchors

CWE-777 — Regular Expression without Anchors: The product uses a regular expression to perform neutralization, but the regular expression is not anchored and may allow malicious or malformed data to slip through.

[Implementation] Be sure to understand both what will be matched and what will not be matched by a regular expression. Anchoring the ends of the expression will allow the programmer to define an allowlist strictly limited to what is matched by the text in the regular expression. If you are using a package that only matches one line by default, ensure that you can match multi-line inputs if necessary.

Source: MITRE CWE corpus.