Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N/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:XSummary
CVE-2026-34932 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Hoppscotch Hoppscotch. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 21th 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-2026-34932 is a stored cross-site scripting (XSS) vulnerability in Hoppscotch, an open-source API development ecosystem, affecting versions prior to 2026.3.0. Classified under CWE-79, the flaw allows malicious scripts to be stored and executed in the context of other users viewing affected content. It carries a CVSS v3.1 base score of 9.3 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N), indicating high severity due to its network accessibility, low attack complexity, lack of required privileges, and potential for cross-origin impact with high confidentiality and integrity effects.
A remote attacker without authentication can exploit this vulnerability by injecting malicious payloads into Hoppscotch, which are then stored and rendered for other users. Exploitation requires user interaction, such as viewing a tampered API request or response, triggering the stored XSS payload. This can lead to cross-site request forgery (CSRF), enabling the attacker to perform actions on behalf of the victim, such as stealing sensitive data (e.g., API keys or session tokens) or modifying application state.
The vulnerability has been addressed in Hoppscotch version 2026.3.0. Security practitioners should update to this patched release, as detailed in the official GitHub release notes (https://github.com/hoppscotch/hoppscotch/releases/tag/2026.3.0) and the corresponding security advisory (https://github.com/hoppscotch/hoppscotch/security/advisories/GHSA-wj4r-hr4h-g98v). No additional mitigations are specified beyond upgrading.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-18540
Vulnerability Data
hoppscotch is an open source API development ecosystem. Prior to version 2026.3.0, there is a stored XSS vulnerability that can lead to CSRF. This issue has been patched in version 2026.3.0.
- 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.