Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2022-31097 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Grafana Grafana. Its CVSS base score is 7.3 (High).
Operationally, ranked in the top 0.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SI-15 (Information Output Filtering) — 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.
Grafana is an open-source monitoring and observability platform affected by a stored cross-site scripting vulnerability in the Unified Alerting feature. The issue impacts versions on the 8.x and 9.x branches prior to 9.0.3, 8.5.9, 8.4.10, and 8.3.10 and is tracked as CWE-79 with a CVSS 3.1 score of 7.3.
An attacker with editor privileges can exploit the flaw to escalate to administrator rights by crafting a malicious alert that executes script when an authenticated administrator clicks a crafted link.
Official advisories and release notes for the patched versions state that upgrades to 9.0.3, 8.5.9, 8.4.10, or 8.3.10 resolve the issue, while workarounds include disabling alerting entirely or reverting to legacy alerting mode. The EPSS score has remained flat at its peak value of 0.4806 with no material post-disclosure rise.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1819
Vulnerability Data
Grafana is an open-source platform for monitoring and observability. Versions on the 8.x and 9.x branch prior to 9.0.3, 8.5.9, 8.4.10, and 8.3.10 are vulnerable to stored cross-site scripting via the Unified Alerting feature of Grafana. An attacker can exploit…
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this vulnerability to escalate privilege from editor to admin by tricking an authenticated admin to click on a link. Versions 9.0.3, 8.5.9, 8.4.10, and 8.3.10 contain a patch. As a workaround, it is possible to disable alerting or use legacy alerting.
- CWE(s)
Related Threats
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Enforces validation of alert content in the Unified Alerting feature to reject or sanitize XSS payloads before storage.
Filters alert output rendered to administrators, neutralizing stored script before it executes on click.
Disables the vulnerable Unified Alerting feature (or reverts to legacy mode) to eliminate the attack surface entirely.
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.