Описание
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.
Отчет
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.
Затронутые пакеты
| Платформа | Пакет | Состояние | Рекомендация | Релиз |
|---|---|---|---|---|
| Red Hat AI Inference Server | rhaiis/vllm-cpu-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaiis/vllm-cuda-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaiis/vllm-neuron-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaiis/vllm-rocm-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaiis/vllm-spyre-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaiis/vllm-tpu-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaii/vllm-cpu-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaii/vllm-cuda-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaii/vllm-gaudi-rhel9 | Fix deferred | ||
| Red Hat AI Inference Server | rhaii/vllm-neuron-rhel9 | Fix deferred |
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Дополнительная информация
Статус:
EPSS
4.8 Medium
CVSS3
Связанные уязвимости
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.
A flaw was found in vLLM, an open-source library for large language mo ...
vLLM: image EXIF Rotation & PNG tRNS Transparency Not Normalized, Causing Mismatch Between Model Input and Expectations
EPSS
4.8 Medium
CVSS3