CVE-2026-102404 in Elasticsearch
Summary
by MITRE • 10/06/2026
Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can submit a specially crafted query that causes uncontrolled memory growth in the query processing engine, resulting in an out-of-memory condition that terminates the Elasticsearch node. The condition can be triggered repeatedly, including by queries embedded in shared resources, causing persistent cluster unavailability.
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Analysis
by VulDB Data Team • 10/07/2026
The vulnerability identified as CWE-400 represents a critical flaw within the Elasticsearch search and analytics engine, specifically manifesting through excessive resource allocation during query processing. This issue stems from an inability of the system to properly bound or limit memory consumption when executing complex queries submitted by authenticated users. While authentication provides a baseline layer of access control, it does not inherently validate the computational cost or memory footprint of the operations requested. Consequently, any user with low-level privileges can exploit this lack of resource governance to trigger uncontrolled growth in heap usage within the query processing engine. This behavior aligns directly with CAPEC-130, which describes an attack pattern where an adversary forces a system to allocate excessive resources, thereby degrading performance or causing failure through exhaustion rather than traditional exploitation techniques like buffer overflows or code injection.
The operational impact of this vulnerability is severe and immediate, primarily resulting in denial of service conditions for the affected Elasticsearch cluster. When a specially crafted query consumes memory beyond available limits, it triggers an out-of-memory error that forces the termination of the specific Elasticsearch node hosting the overloaded shard or index. This process is not limited to single-instance failures; because Elasticsearch clusters rely on distributed coordination and shared resources such as common indices or templates, malicious queries embedded in these shared structures can repeatedly trigger memory exhaustion across multiple nodes simultaneously. The persistence of this condition means that even after a node restarts, if the underlying query logic remains active within shared configurations, the cluster may experience recurring instability, leading to prolonged periods where data retrieval and indexing operations are unavailable to legitimate users.
From an architectural perspective, this flaw highlights gaps in input validation regarding computational complexity rather than just syntax correctness. The system fails to implement effective safeguards such as circuit breakers or strict memory limits for specific query types that are known to be resource-intensive. Without these controls, the engine proceeds with execution until physical memory constraints are breached, at which point the Java Virtual Machine halts the process to prevent corruption of other data structures. This behavior underscores the importance of implementing defense-in-depth strategies where authentication is complemented by robust rate limiting and query complexity analysis mechanisms that operate independently of user privilege levels.
Mitigation efforts must focus on both immediate remediation and long-term architectural improvements. Administrators should immediately apply vendor-provided patches that address this specific memory allocation flaw, ensuring all nodes in the cluster are updated to versions where these limits have been enforced. Additionally, it is critical to review and restrict access to shared resources such as common indices or persistent queries, limiting write privileges only to trusted administrative accounts rather than general authenticated users. Implementing query profiling tools can help identify patterns of excessive resource usage before they reach critical thresholds. Furthermore, configuring Elasticsearch circuit breakers with appropriate thresholds for heap memory usage can provide an additional layer of protection by aborting expensive operations early in the execution phase, thereby preserving system stability and preventing out-of-memory crashes that lead to cluster unavailability.