Elasticsearch ML Inference Heap Corruption via Offset Overflow
CVE-2026-72642 Published on August 13, 2026
Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process
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.
Vulnerability Analysis
CVE-2026-72642 is exploitable with network access, and requires small amount of user privileges. This vulnerability is considered to have a low attack complexity. The potential impact of an exploit of this vulnerability is considered to be very high.
Weakness Type
What is an Untrusted pointer offset Vulnerability?
The program performs pointer arithmetic on a valid pointer, but it uses an offset that can point outside of the intended range of valid memory locations for the resulting pointer.
CVE-2026-72642 has been classified to as an Untrusted pointer offset vulnerability or weakness.
Products Associated with CVE-2026-72642
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Affected Versions
Elasticsearch:- Version 8.19.0, <= 8.19.19 is affected.
- Version 9.4.0, <= 9.4.4 is affected.
- Version 9.5.0 is affected.