Raw vector
CVSS: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:P/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:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-62172 is a high-severity Cross-site Scripting (CWE-79) vulnerability. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 35th 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
- 🇪🇺 ENISA EUVD: EUVD-2025-34249
Vulnerability Data
Home Assistant is open source home automation software that puts local control and privacy first. In versions 2025.1.0 through 2025.10.1, the energy dashboard is vulnerable to stored cross-site scripting. An authenticated user can inject malicious JavaScript code into an energy…
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entity's name field, which is then executed when any user hovers over data points in the energy dashboard graph tooltips. The vulnerability exists because entity names containing HTML are not properly sanitized before being rendered in graph tooltips. This could allow an attacker with authentication to execute arbitrary JavaScript in the context of other users' sessions. Additionally, if an energy provider (such as Tibber) supplies a malicious default name for an entity, the vulnerability can be exploited without direct user action when the default name is used. This issue has been patched in version 2025.10.2. No known workarounds exist.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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.
Secure engineering principles include mandatory output encoding and neutralization of HTML metacharacters to stop injection at the source.
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.
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.