CVE-2026-12491
MEDIUM EPSS 14.9%
Published Jun 17, 20262w ago · Modified Jun 17, 20262w ago
4.8 CVSS 3.1
Published Jun 17, 2026 2w ago
Last Modified Jun 17, 2026 2w ago
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.
CVSS Details
Base Score
Exploitability
Impact
Vector string
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L Attack Vector Network
Attack Complexity High
Privileges Required None
User Interaction None
Scope Unchanged
Confidentiality None
Integrity Low
Availability Low
Threat Intelligence
EPSS Exploit Probability
14.9% percentile
Exploit & Patch Status
No Known Exploit
No Patch Available
Weaknesses 1
CWE-115
References 2
- access.redhat.com https://access.redhat.com/security/cve/CVE-2026-12491
- bugzilla.redhat.com https://bugzilla.redhat.com/show_bug.cgi?id=2489786
Remediation
No remediation data recorded yet
Check vendor advisories and the NVD entry for patch availability.