Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:LSummary
CVE-2026-31938 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Parall Jspdf. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 18th 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-2026-31938 is a cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting the jsPDF JavaScript library for generating PDFs in versions prior to 4.2.1. The issue stems from insufficient sanitization of the user-controlled `options` argument passed to the `output` function, enabling attackers to inject arbitrary HTML, including scripts, into the browser context where the generated PDF is opened. The vulnerability carries a CVSS v3.1 base score of 9.6 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:L), reflecting its high severity due to network accessibility, low attack complexity, and potential for significant confidentiality, integrity, and availability impacts.
An attacker can exploit this vulnerability by supplying malicious values for the `output` options, for instance through a web interface where users submit parameters. These unsanitized values are then passed—either automatically or semi-automatically—to a victim who uses one of the vulnerable `output` method overloads to create and open the PDF in their browser. Successful exploitation allows the injected scripts to execute in the victim's browser context, enabling the attacker to extract or modify sensitive data from that context.
The jsPDF security advisory (GHSA-wfv2-pwc8-crg5), release notes for version 4.2.1, and the fixing commit (87a40bbd07e6b30575196370670b41f264aa78d7) confirm the vulnerability has been patched in jsPDF 4.2.1. As a workaround, developers should sanitize all user input before passing it to the `output` method.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-12755
Vulnerability Data
jsPDF is a library to generate PDFs in JavaScript. Prior to version 4.2.1, user control of the `options` argument of the `output` function allows attackers to inject arbitrary HTML (such as scripts) into the browser context the created PDF is…
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opened in. The vulnerability can be exploited in the following scenario: the attacker provides values for the output options, for example via a web interface. These values are then passed unsanitized (automatically or semi-automatically) to the attack victim. The victim creates and opens a PDF with the attack vector using one of the vulnerable method overloads inside their browser. The attacker can thus inject scripts that run in the victims browser context and can extract or modify secrets from this context. The vulnerability has been fixed in jspdf@4.2.1. As a workaround, sanitize user input before passing it to the output method.
- 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.