CVE-2026-94397 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 improper handling of specific query structures or index configurations. This flaw allows attackers to trigger excessive allocation of system resources such as CPU cycles, memory, or disk I/O without adequate throttling or validation mechanisms in place. The core technical issue lies within the request processing pipeline where complex aggregations, deep pagination requests, or poorly optimized search queries can cause the underlying Lucene engine to consume disproportionate amounts of heap space and computational power. When a malicious actor crafts a specially designed query that forces Elasticsearch to perform expensive operations like sorting large datasets on high-cardinality fields or executing nested aggregations across numerous shards, the system may enter a state of resource exhaustion. This behavior is characteristic of an Excessive Allocation attack vector where the adversary manipulates input parameters to force the application into allocating more resources than intended by its design limits.
The operational impact of this vulnerability is severe and primarily manifests as a denial of service condition for legitimate users. As the Elasticsearch cluster becomes overwhelmed by these resource-intensive operations, response times degrade significantly, leading to timeouts and eventual unavailability of search services. In clustered environments, the strain on one node can propagate across the network due to inter-node communication overheads required for distributed query execution, potentially causing a cascading failure that affects the entire cluster's stability. This disruption prevents critical business functions reliant on real-time data retrieval from operating correctly, resulting in potential financial loss and reputational damage. Furthermore, if the vulnerability allows for sustained resource consumption without automatic recovery mechanisms kicking in promptly, it may lead to node crashes or restarts, causing temporary data inconsistency during the re-indexing process that follows a cluster rebalancing event.
From a threat modeling perspective, this scenario aligns with Common Weakness Enumeration identifier CWE-400 which covers issues related to uncontrolled resource consumption. The specific attack technique employed falls under CAPEC-130 Excessive Allocation, where an attacker exploits the lack of proper input validation and rate limiting to force the system into allocating more resources than it can efficiently manage. This type of vulnerability is often exacerbated by default configurations that may not enforce strict limits on query complexity or resource usage per user session. Attackers typically leverage this weakness through automated tools that generate high-volume, complex queries targeting vulnerable endpoints, effectively turning a legitimate service feature into an attack surface for denial-of-service attacks.
Mitigation strategies must focus on both architectural hardening and operational controls to prevent exploitation of this flaw. Administrators should implement strict rate limiting at the load balancer or API gateway level to cap the number of requests per second from any single source, thereby preventing rapid resource exhaustion. Configuring Elasticsearch-specific settings such as indices.query.bool.max_clause_count can help restrict overly complex boolean queries that are prone to causing performance degradation. Additionally enabling circuit breakers within Elasticsearch ensures that if a query exceeds predefined memory thresholds, it is immediately aborted rather than allowed to consume all available heap space. Regularly updating the software to incorporate patches addressing known resource management issues is essential, as vendors frequently refine these limits based on community feedback and observed attack patterns. Monitoring tools should be deployed to detect anomalous spikes in CPU or memory usage associated with search operations, allowing for proactive intervention before a full denial of service occurs.