CVE-2026-115715 in Elasticsearch
Summary
by MITRE • 11/03/2026
Allocation of Resources Without Limits or Throttling (CWE-770) in Elasticsearch can lead to denial of service via a specially crafted input value that is processed by an affected phonetic encoder of the analysis-phonetic plugin. An authenticated user with permission to write documents to or run searches against an index that uses an affected encoder can cause an Elasticsearch node to exhaust its heap memory. The node can become unresponsive or terminate.
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Analysis
by VulDB Data Team • 11/03/2026
The vulnerability identified as CWE-770, representing Allocation of Resources Without Limits or Throttling, affects the analysis-phonetic plugin within Elasticsearch environments. This specific flaw arises from a deficiency in how the application manages system resources during the processing of phonetic encoding operations. Phonetic encoders are designed to convert text into codes that represent their pronunciation, facilitating fuzzy search capabilities for misspelled words or variations in spelling. However, when an attacker provides a specially crafted input value characterized by extreme length or complex structure, the underlying algorithm fails to impose appropriate constraints on memory allocation. This lack of throttling allows the process to consume resources at an uncontrolled rate, directly leading to a denial of service condition.
From a technical perspective, the core issue lies in the heap memory management of the Elasticsearch node processing the malicious payload. When an authenticated user with write permissions or search privileges targets an index configured with the vulnerable phonetic encoder, they can trigger this resource exhaustion. The input value forces the application to allocate excessive amounts of Java Heap space during the encoding process. Because there are no effective limits on the size or complexity of inputs that can be processed in a single operation, the node's memory capacity is rapidly depleted. This behavior aligns with common patterns observed in software where external data is not sufficiently sanitized or bounded before processing, leading to instability under specific input conditions.
The operational impact of this vulnerability is severe for any organization relying on Elasticsearch for search and analytics workloads. Once a node exhausts its heap memory, it typically becomes unresponsive as the Java Virtual Machine struggles with garbage collection overhead or fails entirely due out-of-memory errors. In many cases, the affected node will terminate abruptly to prevent further corruption of data structures. This results in immediate service disruption for users attempting to query that index. If multiple nodes are compromised through similar vectors, it can lead to cluster-wide instability, loss of search availability, and potential data inconsistency if shards fail over without proper coordination or backup mechanisms being engaged.
Mitigation strategies must focus on both technical controls and operational procedures. Administrators should immediately update the Elasticsearch installation and all associated plugins to versions that include patches for this resource allocation flaw. If updating is not feasible in the short term, restricting access to indices using the phonetic encoder to only trusted users can reduce exposure. Additionally, implementing rate limiting at the network or application gateway level may help mitigate the impact by preventing rapid-fire requests from exhausting resources before a single malicious request completes. Monitoring heap memory usage and setting up alerts for sudden spikes can also provide early warning signs of such attacks in progress.
This vulnerability is categorized under CWE-770, which specifically addresses scenarios where software allocates system resources without appropriate limits or throttling mechanisms. In the context of the MITRE ATT&CK framework, this behavior relates to techniques that aim to disrupt service availability, often falling under resource exhaustion tactics used by adversaries to degrade operational capability. Understanding these classifications helps security teams prioritize remediation efforts and align their defense strategies with industry-standard threat models. By addressing the root cause through code updates and enforcing strict input validation policies, organizations can prevent similar exploitation vectors in future software versions or custom implementations of phonetic analysis features.