CVE-2026-78586 in Kibana
Summary
by MITRE • 09/02/2026
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). An authenticated user with low-level privileges could submit a specially crafted request that causes Kibana to consume an unbounded amount of memory, rendering it unavailable to all users.
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Analysis
by VulDB Data Team • 09/02/2026
The vulnerability identified as CWE-770 represents a critical failure in resource management within the Kibana application ecosystem. This specific flaw allows for the allocation of system resources without appropriate limits or throttling mechanisms, creating an environment where computational assets can be consumed disproportionately by individual requests. In the context of web applications and data visualization platforms like Kibana, such unbounded allocations are particularly dangerous because they directly impact server stability and availability. The absence of strict quotas means that a single operation can trigger cascading memory consumption events that exceed the physical or virtual limits of the hosting environment, leading to system instability or complete failure.
An authenticated user possessing low-level privileges is capable of exploiting this weakness through Excessive Allocation techniques as defined in CAPEC-130. By submitting specially crafted requests designed to maximize data processing requirements, an attacker can force Kibana to allocate unbounded amounts of memory during query execution or visualization rendering processes. This exploitation does not require high administrative rights, which significantly lowers the barrier for entry and increases the potential attack surface. The vulnerability stems from insufficient validation of input parameters that dictate resource consumption, allowing malicious actors to manipulate internal algorithms into performing computationally expensive operations repeatedly or with excessively large datasets than intended by system administrators.
The operational impact of this vulnerability is severe, primarily manifesting as a denial of service condition for all users interacting with the Kibana instance. When memory usage spirals out of control due to excessive allocation, the application may become unresponsive, crash, or trigger automatic restarts by container orchestration systems such as Kubernetes or Docker. This results in significant downtime and loss of availability for critical monitoring dashboards and log analysis tools that organizations rely upon for operational visibility. The impact extends beyond mere inconvenience; it can disrupt incident response capabilities, hinder real-time decision-making processes, and create gaps in security observability during active threat scenarios where continuous access to Kibana is essential.
Mitigation strategies must focus on implementing robust resource governance controls within the application configuration and infrastructure layers. Administrators should enforce strict memory limits at both the JVM level for Java-based components of Kibana and at the container or host OS level using tools like cgroups or Kubernetes resource quotas. Additionally, enabling query size limitations and timeout thresholds can prevent individual requests from consuming excessive resources. Regular auditing of user permissions ensures that even low-privileged accounts are restricted from executing heavy analytical queries against large datasets without oversight. Updating to patched versions where these allocation limits have been hard-coded is the most effective remediation path, alongside deploying Web Application Firewalls capable of detecting and blocking anomalous request patterns indicative of resource exhaustion attacks.