CVE-2026-12491

Description

A flaw was found in vLLM, an open-source library for large language model inference. This vulnerability arises from improper handling of image metadata, specifically EXIF orientation and PNG transparency (tRNS) data, during image processing. When images are converted to RGB, transparency information may be implicitly discarded or remapped, leading to unexpected rendering of transparent pixels and distortion of input content. This can result in the model misinterpreting image content, potentially affecting the integrity of processed data.

Statement

This Moderate flaw in vLLM, as used in Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI, stems from incorrect image metadata handling during processing. Specifically, EXIF orientation and PNG transparency data are not normalized, causing large language models to misinterpret image content. This can lead to a loss of data integrity in processed inputs, as the model's understanding of an image may differ from its intended representation.

Common Vulnerability Scoring System (CVSS) Score Details

Info alert:Important note

CVSS scores for open source components depend on vendor-specific factors (e.g. version or build chain). Therefore, Red Hat's score and impact rating can be different from NVD and other vendors. Red Hat remains the authoritative CVE Naming Authority (CNA) source for its products and services (see Red Hat classifications).

The following CVSS metrics and score provided are preliminary and subject to review.

CVSS v3 Score Breakdown

Red HatNVDcve.org
Base Score4.8N/A4.8
Attack VectorNetworkN/ANetwork
Attack ComplexityHighN/AHigh
Privileges RequiredNoneN/ANone
User InteractionNoneN/ANone
ScopeUnchangedN/AUnchanged
ConfidentialityNoneN/ANone
Integrity ImpactLowN/ALow
Availability ImpactLowN/ALow

Vector

Red Hat: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L

cve.org: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L

Understanding the Weakness (CWE)

Integrity

Technical Impact: Unexpected State

Frequently Asked Questions

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