Summary Users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination.

Details The vulnerability is triggered when the image processor encounters a 1x1 pixel image with shape (1, 1, 3) in HWC (Height, Width, Channel) format. Due to the ambiguous dimensions, the processor incorrectly assumes the image is in CHW (Channel, Height, Width) format with shape (3, H, W). This misinterpretation causes an incorrect calculation of the number of image patches, resulting in a fatal tensor split operation failure.

Crash location: vllm/modelexecutor/models/idefics3.py line 672: python def processimageinput(self, imageinput: ImageInputs) -> torch.Tensor | list[torch.Tensor]: # ... numpatches = imageinput["numpatches"] return [e.flatten(0, 1) for e in imagefeatures.split(numpatches.tolist())]

The split() call fails because the computed numpatches value (17) does not match the actual tensor dimension (9): RuntimeError: splitwithsizes expects splitsizes to sum exactly to 9 (input tensor's size at dimension 0), but got splitsizes=[17]

This unhandled exception terminates the EngineCore process, crashing the server.

Affected Models Any model using the Idefics3 architecture. The vulnerability was tested with HuggingFaceTB/SmolVLM-Instruct.

Impact Denial of service by crashing the engine

Mitigation Validating the input: python def validateimagedimensions(self, imageshape): h, w = imageshape[:2] if len(imageshape) == 3 else imageshape if h < MINIMAGESIZE or w < MINIMAGESIZE: raise ValueError(f"Image dimensions too small: {h}x{w}")

Managing the exception: python try: return [e.flatten(0, 1) for e in imagefeatures.split(numpatches.tolist())] except RuntimeError as e: logger.error(f"Image processing failed: {e}") raise InvalidImageError("Failed to process image features") from e

Fixes

https://github.com/vllm-project/vllm/pull/29881