In the latest version of pytorch/serve, the script 'uploadresultstos3.sh' references the S3 bucket 'benchmarkai-metrics-prod' without ensuring its ownership or confirming its accessibility. This could lead to potential security vulnerabilities or unauthorized access to the bucket if it is not properly secured or claimed by the appropriate entity. The issue may result in data breaches, exposure of proprietary information, or unauthorized modifications to stored data.

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 allowedurls and specifying the model URL to be used. A pull request to warn the user when the default value for allowedurls 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/availableimages.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/availableimages.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)) or directly via email to aws-security@amazon.com. Please do not create a public GitHub issue.

Impact Using the model/workflow management API, there is a chance of uploading potentially harmful archives that contain files that are extracted to any location on the filesystem that is within the process permissions. Leveraging this issue could aid third-party actors in hiding harmful code in open-source/public models, which can be downloaded from the internet, and take advantage of machines running Torchserve.

Patches The ZipSlip issue in TorchServe has been fixed by validating the paths of files contained within a zip archive before extracting them: https://github.com/pytorch/serve/pull/2634

TorchServe release 0.9.0 includes fixes to address the ZipSlip vulnerability: https://github.com/pytorch/serve/releases/tag/v0.9.0

References https://github.com/pytorch/serve/pull/2634 https://github.com/pytorch/serve/releases/tag/v0.9.0

Credit We would like to thank Oligo Security for responsibly disclosing this issue.

If you have any questions or comments about this advisory, we ask that you contact AWS Security via our vulnerability reporting page or directly via email to aws-security@amazon.com. Please do not create a public GitHub issue.