Vulnerability Monitor

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CVE-2026-72642


The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.


Security Impact Summary

This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 8.8, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction requiring only low-level privileges . The vulnerability impacts confidentiality (data exposure), integrity (unauthorized modifications), and availability (service disruption) for affected systems. Impacting 1 product from elastic organizations running these solutions should prioritize assessment and patching.

Historical Context

Reported in 2026, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.


Published

2026-08-13T20:17:24.673

Last Modified

2026-09-01T14:15:50.140

Status

Analyzed

Source

[email protected]

Severity

CVSSv3.1: 8.8 (HIGH)

Weaknesses
  • Type: Secondary
    CWE-823

Affected Vendors & Products
Type Vendor Product Version/Range Vulnerable?
Application elastic elasticsearch < 8.19.20 Yes
Application elastic elasticsearch < 9.4.5 Yes
Application elastic elasticsearch < 9.5.1 Yes

References

How SecUtils Interprets This CVE

SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For elastic's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.