CVE-2026-94398 in Elasticsearch
Summary
by MITRE • 09/26/2026
Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead denial of service via Excessive Allocation (CAPEC-130)
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Analysis
by VulDB Data Team • 09/26/2026
Elasticsearch, a widely deployed distributed search and analytics engine, is susceptible to an uncontrolled resource consumption vulnerability that stems from the application's handling of specific query structures. This flaw allows attackers to craft requests that trigger excessive memory or CPU allocation within the cluster nodes. The core technical issue lies in the parsing and execution phases of complex queries where the system fails to adequately bound the resources required for processing, particularly when dealing with deeply nested logical operations or large-scale aggregations without proper limits enforced at the request level. This behavior aligns directly with CWE-400, which describes situations where a software product does not properly control the allocation and maintenance of a limited resource, thereby allowing an attacker to exhaust system resources such as memory, disk space, or processing time.
The operational impact of this vulnerability is severe, primarily manifesting as a denial of service condition for legitimate users accessing the Elasticsearch cluster. When an attacker exploits this flaw by sending specially crafted requests that induce excessive allocation, it can lead to significant performance degradation across the entire node or cluster. In critical scenarios, the continuous consumption of available memory and CPU cycles causes the affected processes to become unresponsive or crash entirely due to out-of-memory errors. This results in a complete loss of availability for search and analytics services hosted on the compromised infrastructure, disrupting business operations that rely on real-time data retrieval and analysis capabilities.
From an offensive perspective, this vulnerability is often exploited using techniques categorized under CAPEC-130, which involves excessive allocation attacks where the attacker forces the system to allocate more resources than intended or expected. By leveraging this method, adversaries can effectively degrade service quality or cause total outages without necessarily needing to inject malicious code or gain unauthorized access to sensitive data. The attack vector typically requires network connectivity to the Elasticsearch HTTP API and may necessitate authentication depending on the cluster's security configuration, although unauthenticated instances are at significantly higher risk.
To mitigate this vulnerability, organizations must implement strict resource limits within their Elasticsearch configurations. This includes setting appropriate values for thread pool sizes, circuit breaker thresholds, and query complexity restrictions through plugins or native settings like indices.query.bool.max_clause_count. Additionally, deploying Web Application Firewalls with rules specifically designed to detect anomalous query patterns can provide an additional layer of defense by blocking requests that exhibit characteristics of resource exhaustion attacks. Regularly updating Elasticsearch to the latest stable version is also crucial as vendors frequently release patches addressing such logic flaws and improving internal safeguards against uncontrolled resource consumption. Monitoring tools should be configured to alert on unusual spikes in memory usage or query execution times, enabling rapid detection and response to potential exploitation attempts before they result in service disruption.