Cyber Resilience

CVE-2026-44727

XSS in Jupyter Server ≤ 2.20.0

Published
22 June 2026
Modified
22 July 2026
Patch / advisory
CVSS Score v4 9.3
Click a component to see what it means
Raw vectorCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
EPSS Score 0.0031 23th percentile
Risk Priority 70 floored blend · peak EPSS

Summary

CVE-2026-44727 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Jupyter Jupyter Server. Its CVSS base score is 9.3 (Critical).

Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 23th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

Jupyter Server is the backend for Jupyter web applications. Prior to 2.20, the nbconvert HTTP handlers in jupyter_server render user-authored notebook HTML under the Jupyter origin without a sandbox directive in their Content-Security-Policy. Combined with nbconvert.HTMLExporter's default non-sanitizing behavior, a…

more

notebook carrying an HTML payload in a display_data output triggers stored XSS with cookie access, full /api/* authority, and kernel RCE. This vulnerability is fixed in 2.20.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1185 Browser Session Hijacking Collection
Adversaries may take advantage of security vulnerabilities and inherent functionality in browser software to change content, modify user-behaviors, and intercept information as part of various browser session hijacking techniques.
T1539 Steal Web Session Cookie Credential Access
An adversary may steal web application or service session cookies and use them to gain access to web applications or Internet services as an authenticated user without needing credentials.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
T1189 Drive-by Compromise Initial Access
Adversaries may gain access to a system through a user visiting a website over the normal course of browsing.
T1056 Input Capture Collection
Adversaries may use methods of capturing user input to obtain credentials or collect information.
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-40170Same product: Jupyter Jupyter Server
CVE-2025-57769Shared CWE-1021, CWE-79
CVE-2025-0546Shared CWE-1021, CWE-79
CVE-2026-70486Shared CWE-1021, CWE-79
CVE-2023-25837Shared CWE-79
CVE-2023-22438Shared CWE-79
CVE-2023-27614Shared CWE-79
CVE-2023-47164Shared CWE-79
CVE-2023-49145Shared CWE-79
CVE-2023-47853Shared CWE-79

Affected Assets

jupyter
jupyter server
≤ 2.20.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.1.2
  • V1.3.2

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.

Input validation directly enforces neutralization of untrusted data before it reaches web output generation.

Output filtering can catch or sanitize unneutralized script content before it is served to users.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).

PR.PS-02 partial match
prevents

Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

finds

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

prevents

Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.

degrades

Web filtering can block or sandbox untrusted frames, but does not enforce application-level frame-busting or CSP.

prevents

Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.

prevents

Application security requirements can mandate frame-ancestors / X-Frame-Options, directly addressing UI redressing risks.

References