Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:LSummary
CVE-2026-25847 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Jetbrains Pycharm. Its CVSS base score is 8.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 11th 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-25847 is a DOM-based cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting JetBrains PyCharm versions prior to 2025.3.2. The issue resides specifically in the Jupyter viewer page, where malicious input could lead to script execution in the victim's browser context. Published on 2026-02-09, it carries a CVSS v3.1 base score of 8.2 (AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:L), indicating high severity due to its potential for significant data exposure and manipulation.
Remote attackers can exploit this vulnerability over the network without requiring privileges, though it demands high attack complexity and user interaction, such as a victim opening a maliciously crafted Jupyter notebook or URL in the viewer. Upon successful exploitation, the changed scope allows attackers to achieve high confidentiality and integrity impacts—such as stealing sensitive data like session tokens or modifying page content—while causing only low availability disruption.
JetBrains has mitigated the vulnerability in PyCharm 2025.3.2, with details available on their issues fixed page at https://www.jetbrains.com/privacy-security/issues-fixed/. Security practitioners should ensure users upgrade to this version or later to prevent exploitation.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-6391
Vulnerability Data
In JetBrains PyCharm before 2025.3.2 a DOM-based XSS on Jupyter viewer page was possible
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
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.
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).
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.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
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.
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.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.