CVE-2026-63261
Summary
by MITRE • 07/22/2026
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.
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Analysis
by VulDB Data Team • 07/22/2026
This vulnerability represents a critical resource exhaustion flaw within the kibana platform that manifests as an uncontrolled resource consumption issue classified under CWE-400. The vulnerability specifically impacts the machine learning functionality of kibana where a maliciously crafted request can trigger excessive memory allocation patterns that ultimately lead to system-wide denial of service conditions. The attack vector requires only low-privileged authentication access, making it particularly dangerous as it can be exploited by users with minimal permissions who typically would not have the capability to cause significant system disruption.
The technical implementation of this vulnerability occurs within kibana's machine learning processing components where insufficient input validation and resource management controls allow attackers to manipulate allocation requests that grow exponentially in memory usage. When a specially crafted request is processed through the ml feature, it triggers a cascade of resource consumption that bypasses normal memory limits and allocation safeguards. This behavior aligns with CAPEC-130 classification for excessive allocation attacks where attacker-controlled inputs directly influence system resource consumption patterns without proper bounds checking or resource accounting mechanisms.
The operational impact of this vulnerability extends far beyond simple service interruption as it affects the entire kibana ecosystem and potentially impacts downstream systems that depend on its availability. Once memory exhaustion occurs, the affected kibana instance becomes completely unresponsive to all users and requests, creating a complete denial of service condition that can persist until manual intervention or system restart occurs. This type of vulnerability is particularly concerning in enterprise environments where kibana serves as a central monitoring and analytics platform for security operations and system administration tasks.
Organizations should implement immediate mitigations including input validation controls, resource limits on machine learning features, and monitoring systems to detect unusual memory consumption patterns. The vulnerability demonstrates the importance of proper resource management in distributed systems and aligns with ATT&CK techniques focusing on resource exhaustion and denial of service attacks. Security teams must also consider implementing rate limiting and allocation quotas for privileged features while maintaining comprehensive logging of machine learning activity to detect potential exploitation attempts. Regular security assessments should include testing for similar resource consumption patterns in other kibana components and related software systems to prevent similar vulnerabilities from being overlooked in the broader attack surface.