Medium severity · published Jun 17, 2026
CVE-2026-12491
Vllm: vllm: image exif rotation & png trns transparency not normalized, causing mismatch between model input and expectations
4.8
CVSS
0.2%
EPSS
No
On KEV
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 vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:L
Attack vector
Network
Complexity
High
Privileges
None
User interaction
None
Confidentiality
None
Integrity
Low
Availability
Low
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.2% · higher than 15% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
RH
Red Hat AI Inference Server · Red Hat
All versions, or not specified · source: vendor
RH
Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat
All versions, or not specified · source: vendor
RH
Red Hat OpenShift AI (RHOAI) · Red Hat
All versions, or not specified · source: vendor
VL
vLLM · vllm-project
0.11.0 before 0.24.0 · source: vendor
Affected product
RH
Red Hat AI Inference Server
Red Hat
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