CVE-2023-43654

## Impact **Remote Server-Side Request Forgery (SSRF)** **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`. **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change. ## Patches ## TorchServe release 0.8.2 includes fixes to address the previously listed issue: https://github.com/pytorch/serve/releases/tag/v0.8.2 **Tags for upgraded DLC release** User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2: x86 GPU * v1.9-pt-ec2-2.0.1-inf-gpu-py310 * v1.8-pt-sagemaker-2.0.1-inf-gpu-py310 x86 CPU * v1.8-pt-ec2-2.0.1-inf-cpu-py310 * v1.7-pt-sagemaker-2.0.1-inf-cpu-py310 Graviton * v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310 * v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310 Neuron * 1.13.1-neuron-py310-sdk2.13.2-ubuntu20.04 * 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04 * 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04 The full DLC image URI details can be found at: https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images ## References https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296 https://github.com/pytorch/serve/pull/2534 https://github.com/pytorch/serve/releases/tag/v0.8.2 https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images ## Credit We would like to thank Oligo Security for responsibly disclosing this issue and working with us on its resolution. If you have any questions or comments about this advisory, we ask that you contact AWS/Amazon Security via our [vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting[)](https://aws.amazon.com/security/vulnerability-reporting)) or directly via email to [aws-security@amazon.com](mailto:aws-security@amazon.com). Please do not create a public GitHub issue.

## Impact **Remote Server-Side Request Forgery (SSRF)** **Issue**: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and sensitive data. This issue is present in versions `0.1.0` to `0.8.1`. **Mitigation**: The user is able to load the model of their choice from any URL that they would like to use. The user of TorchServe is responsible for configuring both the [allowed_urls](https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296) and specifying the model URL to be used. A pull request to warn the user when the default value for `allowed_urls` is used has been merged - https://github.com/pytorch/serve/pull/2534. TorchServe release `0.8.2` includes this change. ## Patches ## TorchServe release 0.8.2 includes fixes to address the previously listed issue: https://github.com/pytorch/serve/releases/tag/v0.8.2 **Tags for upgraded DLC release** User can use the following new image tags to pull DLCs that ship with patched TorchServe version 0.8.2: x86 GPU * v1.9-pt-ec2-2.0.1-inf-gpu-py310 * v1.8-pt-sagemaker-2.0.1-inf-gpu-py310 x86 CPU * v1.8-pt-ec2-2.0.1-inf-cpu-py310 * v1.7-pt-sagemaker-2.0.1-inf-cpu-py310 Graviton * v1.7-pt-graviton-ec2-2.0.1-inf-cpu-py310 * v1.5-pt-graviton-sagemaker-2.0.1-inf-cpu-py310 Neuron * 1.13.1-neuron-py310-sdk2.13.2-ubuntu20.04 * 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04 * 1.13.1-neuronx-py310-sdk2.13.2-ubuntu20.04 The full DLC image URI details can be found at: https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images ## References https://github.com/pytorch/serve/blob/b3eced56b4d9d5d3b8597aa506a0bcf954d291bc/docs/configuration.md?plain=1#L296 https://github.com/pytorch/serve/pull/2534 https://github.com/pytorch/serve/releases/tag/v0.8.2 https://github.com/aws/deep-learning-containers/blob/master/available_images.md#available-deep-learning-containers-images ## Credit We would like to thank Oligo Security for responsibly disclosing this issue and working with us on its resolution. If you have any questions or comments about this advisory, we ask that you contact AWS/Amazon Security via our [vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting[)](https://aws.amazon.com/security/vulnerability-reporting)) or directly via email to [aws-security@amazon.com](mailto:aws-security@amazon.com). Please do not create a public GitHub issue.

TorchServe es una herramienta para servir y escalar modelos de PyTorch en producción. La configuración predeterminada de TorchServe carece de una validación de entrada adecuada, lo que permite a terceros invocar solicitudes de descarga HTTP remotas y escribir archivos en el disco. Se podría aprovechar este problema para comprometer la integridad del sistema y los datos confidenciales. Este problema está presente en las versiones 0.1.0 a 0.8.1. Un usuario puede cargar el modelo de su elección desde cualquier URL que desee utilizar. El usuario de TorchServe es responsable de configurar las URL permitidas y especificar la URL modelo que se utilizará. En PR #2534 se fusionó una solicitud de extracción para advertir al usuario cuando se utiliza el valor predeterminado para Allow_urls. La versión 0.8.2 de TorchServe incluye este cambio. Se recomienda a los usuarios que actualicen. No se conocen workarounds para este problema.