Headline
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 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)) or directly via email to [email protected]. 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 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 - pytorch/serve#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
pytorch/serve#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)) or directly via email to [email protected]. Please do not create a public GitHub issue.
References
- GHSA-8fxr-qfr9-p34w
- https://nvd.nist.gov/vuln/detail/CVE-2023-43654
- pytorch/serve#2534
- https://github.com/pytorch/serve/releases/tag/v0.8.2
Related news
The PyTorch model server contains multiple vulnerabilities that can be chained together to permit an unauthenticated remote attacker arbitrary Java code execution. The first vulnerability is that the management interface is bound to all IP addresses and not just the loop back interface as the documentation suggests. The second vulnerability (CVE-2023-43654) allows attackers with access to the management interface to register MAR model files from arbitrary servers. The third vulnerability is that when an MAR file is loaded, it can contain a YAML configuration file that when deserialized by snakeyaml, can lead to loading an arbitrary Java class.
Cybersecurity researchers have disclosed multiple critical security flaws in the TorchServe tool for serving and scaling PyTorch models that could be chained to achieve remote code execution on affected systems. Israel-based runtime application security company Oligo, which made the discovery, has coined the vulnerabilities ShellTorch. "These vulnerabilities [...] can lead to a full chain Remote
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.