CVE-2026-63136 in Elasticsearch
Summary
by MITRE • 07/22/2026
Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead to denial of service via Excessive Allocation (CAPEC-130). A user with search privileges can submit a specially crafted search request that causes a data node to exhaust available heap memory, resulting in node unavailability and cluster degradation. An attacker could leverage this vulnerability to cause cluster downtime requiring manual intervention to restore service.
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Analysis
by VulDB Data Team • 07/22/2026
Elasticsearch represents one of the most widely deployed distributed search and analytics engines in enterprise environments, serving as the backbone for log analysis, monitoring systems, and application search functionality across countless organizations. The platform's architecture relies heavily on heap memory allocation for processing complex search queries, maintaining indexing operations, and handling concurrent requests from multiple clients. When properly configured, Elasticsearch nodes can efficiently manage resources through various memory management mechanisms including garbage collection tuning and query execution limits. However, the vulnerability described herein exploits fundamental aspects of how the system handles resource allocation during search operations, creating a critical weakness that undermines system availability.
The technical flaw manifests as an uncontrolled resource consumption scenario where maliciously crafted search requests can trigger excessive heap memory allocation within Elasticsearch data nodes. This occurs when specific query patterns bypass normal resource limits and memory constraints that should typically prevent such allocations from overwhelming system resources. The vulnerability specifically relates to how Elasticsearch processes certain types of queries that require substantial memory for intermediate results, aggregations, or complex filtering operations. When these queries are submitted by users with legitimate search privileges, they can cause the JVM heap memory to rapidly expand beyond its configured limits, leading to out-of-memory errors and subsequent node crashes or unresponsiveness.
The operational impact of this vulnerability extends far beyond simple performance degradation, creating a severe denial of service condition that can compromise entire clusters. When a data node becomes unavailable due to excessive memory allocation, the cluster experiences partial or complete service disruption depending on the deployment configuration and replication settings. The affected node typically requires manual intervention including restart procedures, garbage collection tuning, or even complete cluster reconfiguration to restore normal operations. This vulnerability is particularly dangerous because it can be exploited by legitimate users who already possess search privileges, making detection and prevention more challenging within typical security monitoring frameworks.
Mitigation strategies for this vulnerability should focus on implementing comprehensive resource management controls within Elasticsearch configurations. Organizations must establish strict query execution limits including maximum query time thresholds, memory usage caps per request, and aggregation size restrictions to prevent single queries from consuming excessive resources. The implementation of proper monitoring and alerting systems becomes critical for detecting anomalous resource consumption patterns before they escalate into full service disruptions. Additionally, the use of Elasticsearch's built-in security features including role-based access controls and query validation mechanisms can help limit the potential impact of malicious requests. From an operational standpoint, regular capacity planning exercises and performance testing should be conducted to ensure that clusters can handle peak loads without resource exhaustion scenarios.
This vulnerability aligns with several key cybersecurity frameworks and threat modeling concepts including the Common Weakness Enumeration classification of CWE-400 which specifically addresses uncontrolled resource consumption vulnerabilities. The attack pattern described also corresponds to CAPEC-130's concept of excessive allocation where attackers exploit legitimate system functionality to cause resource exhaustion. Organizations implementing Elasticsearch solutions should consider the ATT&CK framework's resource exhaustion tactics and techniques when designing their security monitoring and response capabilities. The vulnerability demonstrates how seemingly benign operational features can be weaponized to create significant service disruption, emphasizing the importance of defensive measures including input validation, resource limiting, and comprehensive system monitoring as outlined in industry best practices for distributed systems security.