CVE-2026-72642

highpriority · track · 0EPSS 0.006 · 49 %ileDEEUUS
grimoire priorityP2 · Scheduled
Exploitable?
—
no known exploitation
Likely / severe?
0.60% (49th) EPSS
CVSS 8.8 · high
Is it mine?
15 products
Check your exposure
Who says so?
5 sources
cnnvd · enisa_euvd · cert_bund · nvd

EPSS · scored 2026-10-27

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.

01

cvss

Technical severity, 0–10. One row per scoring source.

8.8
010

base score 0–10 · higher = more severe · hover a vector to decode

v3.18.8highCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:Hsecondary · ENISA EU
v3.18.8highCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:Hsecondary · security@elastic.coexploitability 2.8 · impact 5.9
v3.18.1highCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H/E:Usecondary · CVSS-BT (t0sche)
02

ssvc

A stakeholder decision — act, attend, or track — from exploitation and impact.

Exploitation: none · Automatable: no · Technical Impact: totalcoordinatorcisa_ssvcUS
03

weaknesses

The CWE weakness classes this flaw belongs to.

04

affected products (15)

Products and version ranges known to be affected.

05

advisories (2)

Vendor and ecosystem advisories describing this.

  • WID-SEC-W-2026-2841Elasticsearch: Mehrere SchwachstellenDeutsch2026-08-13csaf · cert_bundDE

    Ein Angreifer kann mehrere Schwachstellen in Elasticsearch ausnutzen, um beliebigen Programmcode auszuführen, und um einen Denial of Service Angriff durchzuführen.

  • BIT-elasticsearch-2026-72642Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process2026-08-19vendor · osvUS

    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.

06

also known as (3)

Other names and identifiers for this entity.

07

sources

Which sources assert this record, and when.

published 2026-08-13modified 2026-09-01
08

references (4)

External citations — patches, advisories, analysis.

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