### 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/model_executor/models/idefics3.py` line 672: ```python def _process_image_input(self, image_input: ImageInputs) -> torch.Tensor | list[torch.Tensor]: # ... num_patches = image_input["num_patches"] return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())] ``` The `split()` call fails because the computed `num_patches` value (17) does not match the actual tensor dimension (9): ``` RuntimeError: split_with_sizes expects split_sizes to sum exactly to 9 (input tensor's size at dimension 0), but got split_sizes=[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 _validate_image_dimensions(self, image_shape): h, w = image_shape[:2] if len(image_shape) == 3 else image_shape if h < MIN_IMAGE_SIZE or w < MIN_IMAGE_SIZE: raise ValueError(f"Image dimensions too small: {h}x{w}") ``` Managing the exception: ```python try: return [e.flatten(0, 1) for e in image_features.split(num_patches.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
### 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/model_executor/models/idefics3.py` line 672: ```python def _process_image_input(self, image_input: ImageInputs) -> torch.Tensor | list[torch.Tensor]: # ... num_patches = image_input["num_patches"] return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())] ``` The `split()` call fails because the computed `num_patches` value (17) does not match the actual tensor dimension (9): ``` RuntimeError: split_with_sizes expects split_sizes to sum exactly to 9 (input tensor's size at dimension 0), but got split_sizes=[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 _validate_image_dimensions(self, image_shape): h, w = image_shape[:2] if len(image_shape) == 3 else image_shape if h < MIN_IMAGE_SIZE or w < MIN_IMAGE_SIZE: raise ValueError(f"Image dimensions too small: {h}x{w}") ``` Managing the exception: ```python try: return [e.flatten(0, 1) for e in image_features.split(num_patches.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
Update vllm to 0.12.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions affects vllm (pip). Severity is medium. ### 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/model_executor/models/idefics3.py` line 672: ```python def _process_image_input(self, image_input: ImageInputs) -> torch.Tensor | list[torch.Tensor]: # ... num_patches = image_input["num_patches"] return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())] ``` The `split()` call fails because the computed `num_patches` value (17) does not match the actual tensor dimension (9): ``` RuntimeError: split_with_sizes expects split_sizes to sum exactly to 9 (input tensor's size at dimension 0), but got split_sizes=[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 _validate_image_dimensions(self, image_shape): h, w = image_shape[:2] if len(image_shape) == 3 else image_shape if h < MIN_IMAGE_SIZE or w < MIN_IMAGE_SIZE: raise ValueError(f"Image dimensions too small: {h}x{w}") ``` Managing the exception: ```python try: return [e.flatten(0, 1) for e in image_features.split(num_patches.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
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.6.4,<0.12.0 | 0.12.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate vllm to 0.12.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanvLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions affects vllm (pip). Severity is medium. ### 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/model_executor/models/idefics3.py` line 672: ```python def _process_image_input(self, image_input: ImageInputs) -> torch.Tensor | list[torch.Tensor]: # ... num_patches = image_input["num_patches"] return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())] ``` The `split()` call fails because the computed `num_patches` value (17) does not match the actual tensor dimension (9): ``` RuntimeError: split_with_sizes expects split_sizes to sum exactly to 9 (input tensor's size at dimension 0), but got split_sizes=[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 _validate_image_dimensions(self, image_shape): h, w = image_shape[:2] if len(image_shape) == 3 else image_shape if h < MIN_IMAGE_SIZE or w < MIN_IMAGE_SIZE: raise ValueError(f"Image dimensions too small: {h}x{w}") ``` Managing the exception: ```python try: return [e.flatten(0, 1) for e in image_features.split(num_patches.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
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| vllmpip | >=0.6.4,<0.12.0 | 0.12.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard