CVE-2026-72674 in Kibana
Summary
by MITRE • 08/13/2026
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). A user-supplied list of document fields accepted by the Kibana Playground for RAG feature was neither bounded in length nor de-duplicated before it was used to assemble the response for each matching document. A single crafted request could therefore make Kibana build a response far larger than the data it was derived from, and the resulting processing and memory pressure exhausts the resources of the Kibana instance.
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Analysis
by VulDB Data Team • 08/13/2026
The vulnerability described represents a critical resource allocation flaw that fundamentally undermines system stability and availability through uncontrolled memory consumption patterns. This issue manifests within Kibana's Playground feature designed for Retrieval Augmented Generation capabilities, where the system processes user-provided document field lists without implementing any form of input validation or resource limiting mechanisms. The absence of bounds checking on input parameters creates an environment where malicious actors can exploit the system by submitting excessively large field lists that dramatically amplify the processing overhead required to generate responses.
The technical implementation flaw stems from inadequate input sanitization and resource management within the RAG processing pipeline. When users provide lists of document fields for processing, the system fails to enforce length constraints or perform deduplication operations before constructing response objects. This design oversight allows attackers to craft requests containing thousands of field references that, when processed, result in exponential growth in memory allocation requirements. The vulnerability specifically exploits the lack of throttling mechanisms that would normally prevent such unbounded resource consumption patterns from overwhelming system resources.
The operational impact of this vulnerability extends beyond simple denial of service conditions to potentially compromise the entire Kibana instance and surrounding infrastructure. As the system attempts to process increasingly large field lists, memory exhaustion occurs progressively, eventually leading to complete system crashes or severe performance degradation that renders the platform unusable for legitimate users. The amplification effect means that a single malicious request can generate response sizes orders of magnitude larger than the original input data, consuming resources at an unsustainable rate that can affect other services running on the same infrastructure.
This vulnerability directly maps to CWE-770 allocation of resources without limits or throttling, which is classified as a fundamental resource management flaw in software development practices. The issue also aligns with CAPEC-130 excessive allocation patterns that specifically target denial of service through resource exhaustion attacks. From an ATT&CK framework perspective, this represents a resource exhaustion technique that can be categorized under the privilege escalation and denial of service attack vectors. The vulnerability demonstrates poor input validation practices and inadequate defensive programming techniques that are commonly exploited in modern cyber attacks targeting enterprise search and analytics platforms.
Mitigation strategies should focus on implementing strict input validation controls including maximum field list length limits, automatic deduplication of field references, and memory consumption monitoring mechanisms. System administrators should configure resource limits and timeouts to prevent unbounded processing of user inputs, while also implementing rate limiting to control the frequency of requests that can be processed within a given time period. Additionally, regular security audits should be conducted to identify similar resource allocation issues across other features within the Kibana platform and related Elasticsearch components. The implementation of automated monitoring solutions that detect unusual memory consumption patterns can provide early warning systems for potential exploitation attempts, while also enabling incident response teams to quickly identify and contain such attacks before they cause significant system disruption.