Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-21720 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Grafana Grafana. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Exhaustion Flood (T1499.001); ranked at the 46th 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 SC-24 (Fail in Known State) — 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-2026-21720 is a denial-of-service vulnerability in Grafana affecting the /avatar/:hash endpoint. Every uncached request to this endpoint spawns a goroutine to refresh the corresponding Gravatar image. When the refresh operation queues up in the 10-slot worker queue and exceeds the three-second handler timeout, the handler stops listening for the result, leaving the goroutine blocked indefinitely while attempting to send on an unbuffered channel. Under sustained traffic with random hashes, this repeatedly triggers timeouts, causing goroutine counts to grow linearly, exhausting memory and crashing Grafana instances on some systems. The issue maps to CWE-400 (Uncontrolled Resource Consumption) and CWE-703 (Improper Check or Handling of Exceptional Conditions), with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
Any unauthenticated attacker with network access to a vulnerable Grafana instance can exploit this by sending a high volume of requests to /avatar/:hash with random or uncached hashes. This low-complexity attack requires no privileges or user interaction and leads to resource exhaustion, rendering the service unavailable through memory depletion and process crashes.
The official Grafana security advisory provides details on patches and mitigation steps at https://grafana.com/security/security-advisories/CVE-2026-21720.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-4841
Vulnerability Data
Every uncached /avatar/:hash request spawns a goroutine that refreshes the Gravatar image. If the refresh sits in the 10-slot worker queue longer than three seconds, the handler times out and stops listening for the result, so that goroutine blocks forever…
more
trying to send on an unbuffered channel. Sustained traffic with random hashes keeps tripping this timeout, so goroutine count grows linearly, eventually exhausting memory and causing Grafana to crash on some systems.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing resource releases through dynamic analysis or stress testing.
Enforces failure to a known state while preserving required properties, limiting impact of unhandled exceptions.
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Mandates explicit fail-safe procedures triggered by indicated failures, structurally preventing unhandled exceptional conditions.
Requiring documented development standards and tools can mandate explicit resource-release patterns in code.
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.
Lifecycle management explicitly requires handling resources through end-of-life including release.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Secure SDLC practices explicitly require anticipating and correctly handling exceptional conditions during design and coding.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
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.
Explicit information-deletion requirements directly address timely release of resources after use.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.