CVE-2026-12491

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.

CVSS v3 4.8 MEDIUM

4.8^/10

CVSS v3 : MEDIUM

Vector :

Exploitability : 2.2 / Impact : 2.5

Attack Vector NETWORK

Attack Complexity HIGH

Privileges Required NONE

User Interaction NONE

Confidentiality Impact NONE

Integrity Impact LOW

Availability Impact LOW

Scope UNCHANGED

References

Link	Resource
https://access.redhat.com/security/cve/CVE-2026-12491
https://bugzilla.redhat.com/show_bug.cgi?id=2489786

Configurations

No configuration.

History

17 Jun 2026, 16:14

Type	Values Removed	Values Added
New CVE

Information

Published : 2026-06-17 13:20

Updated : 2026-07-07 09:16

NVD link : CVE-2026-12491

Mitre link : CVE-2026-12491

CVE.ORG link : CVE-2026-12491

JSON object : View

Products Affected

No product.

CWE

CWE-115

Misinterpretation of Input

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"source": "secalert@redhat.com"}, {"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2489786", "source": "secalert@redhat.com"}], "vulnStatus": "Awaiting Analysis", "weaknesses": [{"type": "Secondary", "source": "secalert@redhat.com", "description": [{"lang": "en", "value": "CWE-115"}]}], "descriptions": [{"lang": "en", "value": "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."}], "lastModified": "2026-07-07T09:16:29.103", "sourceIdentifier": "secalert@redhat.com"}