Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:N/SC:L/SI:L/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-2026-40179 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Prometheus Prometheus. Its CVSS base score is 5.3 (Medium).
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-23123
Vulnerability Data
Prometheus is an open-source monitoring system and time series database. Versions 3.0 through 3.5.1 and 3.6.0 through 3.11.1 have stored cross-site scripting vulnerabilities in multiple components of the Prometheus web UI where metric names and label values are injected into…
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innerHTML without escaping. In both the Mantine UI and old React UI, chart tooltips on the Graph page render metric names containing HTML/JavaScript without sanitization. In the old React UI, the Metric Explorer fuzzy search results use dangerouslySetInnerHTML without escaping, and heatmap cell tooltips interpolate le label values without sanitization. With Prometheus v3.x defaulting to UTF-8 metric and label name validation, characters like <, >, and " are now valid in metric names and labels. An attacker who can inject metrics via a compromised scrape target, remote write, or OTLP receiver endpoint can execute arbitrary JavaScript in the browser of any Prometheus user who views the metric in the Graph UI, potentially enabling configuration exfiltration, data deletion, or Prometheus shutdown depending on enabled flags. This issue has been fixed in versions 3.5.2 and 3.11.2. If developers are unable to immediately update, the following workarounds are recommended: ensure that the remote write receiver (--web.enable-remote-write-receiver) and the OTLP receiver (--web.enable-otlp-receiver) are not exposed to untrusted sources; verify that all scrape targets are trusted and not under attacker control; avoid enabling admin or mutating API endpoints (e.g., --web.enable-admin-api or --web.enable-lifecycle) in environments where untrusted data may be ingested; and refrain from clicking untrusted links, particularly those containing functions such as label_replace, as they may generate poisoned label names and values.
- 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.