CVE-2026-94408 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 how certain complex queries are parsed and executed by the underlying Lucene indexing library. When a user submits a specially constructed search request, particularly one involving deep nesting of boolean clauses or unbounded wildcards, the engine may attempt to allocate significantly more resources than anticipated during the query planning phase. This behavior is not limited to high-volume production environments but can be triggered even with relatively small payloads if the structural complexity forces the system into an inefficient execution path that fails to respect internal resource limits effectively.
The operational impact of this vulnerability is primarily a denial of service against the Elasticsearch cluster and its dependent services. By exploiting this flaw, an attacker can cause individual nodes or entire clusters to become unresponsive due to memory exhaustion or CPU saturation. In a distributed environment where multiple tenants or applications rely on shared search infrastructure, such an event can lead to cascading failures across connected systems. The service degradation manifests as increased latency for legitimate queries, timeouts in API responses, and potentially the crash of Java Virtual Machine processes running Elasticsearch instances. This disruption affects data availability and integrity checks that depend on real-time indexing capabilities, thereby undermining the reliability of any business logic or monitoring dashboards built upon this platform.
From a threat modeling perspective, this vulnerability aligns with Common Weakness Enumeration identifier CWE-400, which describes issues where an entity does not properly control the allocation and maintenance of a limited resource, leading to exhaustion. The attack vector utilized here corresponds closely to CAPEC-130, Excessive Allocation, wherein the attacker forces the system to allocate more resources than necessary or expected for normal operation. Furthermore, this behavior can be mapped to MITRE ATT&CK techniques related to Resource Hijacking and Denial of Service, specifically within the context of cloud infrastructure exploitation. Attackers leverage these weaknesses not necessarily to steal data but to disrupt service availability, which is a critical component of the CIA triad in security frameworks.
Mitigation strategies for this vulnerability focus on both configuration hardening and software updates. Administrators should ensure that Elasticsearch instances are updated to versions where patches addressing query complexity limits have been applied. It is crucial to configure resource controls such as heap size limitations and thread pool restrictions appropriately based on the available hardware capacity, ensuring that no single request can monopolize system resources. Additionally, implementing rate limiting at the network or application gateway level can help mitigate the impact by restricting the frequency of complex queries from any single source. Deploying Web Application Firewalls with rules designed to detect anomalous query patterns may also provide an additional layer of defense against exploitation attempts targeting this specific resource consumption flaw.