In the latest version of pytorch/serve, the script 'upload_results_to_s3.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.
TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. TorchServe 's check on allowed_urls configuration can be by-passed if the URL contains characters such as ".." but it does not prevent the model from being downloaded into the model store. Once a file is downloaded, it can be referenced without providing a URL the second time, which effectively bypasses the allowed_urls security check. Customers using PyTorch inference Deep Learning Containers (DLC) through Amazon SageMaker and EKS are not affected. This issue in TorchServe has been fixed by validating the URL without characters such as ".." be
TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production. In affected versions the two gRPC ports 7070 and 7071, are not bound to [localhost](http://localhost/) by default, so when TorchServe is launched, these two interfaces are bound to all interfaces. Customers using PyTorch inference Deep Learning Containers (DLC) through Amazon SageMaker and EKS are not affected. This issue in TorchServe has been fixed in PR #3083. TorchServe release 0.11.0 includes the fix to address this vulnerability. Users are advised to upgrade. There are no known workarounds for this vulnerability.
TorchServe is a tool for serving and scaling PyTorch models in production. Starting in version 0.1.0 and prior to version 0.9.0, 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. The ZipSlip issue in TorchServe has been fixed by validating the paths of files contained within a zip archive
## Impact
**Use of Open Source Library potentially exposed to RCE**
**Issue**: Use of a version of the SnakeYAML `v1.31 `open source library with multiple issues that potentially exposes the user to unsafe deserialization of Java objects. This could allow third parties to execute arbitrary code on the target system. This issue is present in versions `0.3.0` to `0.8.1`.
**Mitigation**: A pull request to address this issue has been merged - https://github.com/pytorch/serve/pull/2523. TorchServe release `0.8.2` includes this fix.
## Patches
## TorchServe release 0.8.2 includes fixes to address the previously listed issue:
https://git
TorchServe is a tool for serving and scaling PyTorch models in production. 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. A 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 and specifying the model URL to be used. A pull request to warn the user when the default value for a