CVE-2026-72645 in Elasticsearch
Summary
by MITRE • 08/13/2026
Memory Allocation with Excessive Size Value (CWE-789) in Elasticsearch can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated user holding only read privileges on a single index can submit one small, specially crafted search request that causes an excessively large memory allocation, exhausting the JVM heap and terminating the affected node.
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Analysis
by VulDB Data Team • 08/13/2026
Memory allocation with excessive size value represents a critical vulnerability pattern where applications fail to properly validate or limit memory allocation requests, leading to resource exhaustion attacks. This specific weakness manifests when systems accept user-supplied parameters that directly influence memory allocation decisions without adequate bounds checking or size validation. The vulnerability class falls under CWE-789 which specifically addresses scenarios where programs allocate memory based on untrusted input values that can exceed reasonable limits, creating opportunities for denial of service conditions through resource consumption attacks.
The exploitation mechanism in Elasticsearch involves authenticated users leveraging read privileges on a single index to submit carefully constructed search requests that trigger excessive memory allocation patterns. This particular attack vector demonstrates how seemingly benign operations can be weaponized when proper input validation and resource management controls are absent from the system architecture. The vulnerability operates at the JVM heap level where memory allocation requests bypass normal constraints, allowing malicious actors to consume available heap space rapidly and cause node termination.
This type of denial of service attack directly aligns with CAPEC-130 which describes excessive allocation scenarios where attackers manipulate resource allocation parameters to exhaust system resources. The operational impact extends beyond simple service disruption as it can affect entire clusters when nodes become unresponsive due to heap exhaustion. The vulnerability's severity increases significantly because it requires minimal privileges and can be executed through standard read operations, making it particularly dangerous in multi-tenant environments where users may have varying levels of access.
The technical implementation involves Elasticsearch's internal memory management systems that process user requests without sufficient validation of allocation parameters. Attackers can craft search queries containing large array sizes or complex nested structures that force the system to allocate memory proportional to these inputs, often without proper bounds checking. This creates a situation where legitimate operations become impossible as system resources are consumed by malicious allocation patterns, leading to cascading failures across the cluster infrastructure.
Mitigation strategies must address both immediate defensive measures and architectural improvements to prevent similar vulnerabilities. Organizations should implement strict memory allocation limits within Elasticsearch configurations, enforce query complexity restrictions, and establish monitoring systems that detect unusual memory consumption patterns. The solution involves establishing proper input validation at multiple layers including application-level parameter checking, JVM heap size limitations, and cluster-wide resource management policies. Additionally, implementing rate limiting and query timeout mechanisms can prevent single requests from consuming excessive resources while maintaining system availability for legitimate operations.
Security frameworks such as the OWASP Top Ten and NIST cybersecurity guidelines emphasize the importance of input validation and resource management in preventing memory allocation attacks. The vulnerability demonstrates how insufficient attention to resource constraints during application development can create exploitable conditions that bypass traditional access control mechanisms. Regular security assessments and code reviews focusing on memory management patterns should be implemented to identify similar weaknesses across the software stack, ensuring comprehensive protection against both current and emerging threats in the evolving cybersecurity landscape.