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CVE-2023-43654

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Advisory Summaries

github_osv/GHSA-8fxr-qfr9-p34w

TorchServe Server-Side Request Forgery vulnerability ## 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.

gitlab/pypi/torchserve/CVE-2023-43654

Server-Side Request Forgery (SSRF) 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 allowed_urls is used has been merged in PR #2534. TorchServe release 0.8.2 includes this change. Users are advised to upgrade. There are no known workarounds for this issue.

pypa/torchserve/PYSEC-2026-553

TorchServe Server-Side Request Forgery vulnerability ## 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.

pysec/PYSEC-2026-553

TorchServe Server-Side Request Forgery vulnerability ## 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.