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CVE-2023-43654

CRITICAL
Published 2023-09-28T22:10:09.497Z
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CVSS Score

V3.1
10.0
/10
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.927
probability
of exploitation in the wild

There is a 92.7% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.997
Higher than 99.7% of all CVEs

Attack Vector Metrics

Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
NONE
Scope
CHANGED

Impact Metrics

Confidentiality
HIGH
Integrity
HIGH
Availability
HIGH

Description

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.

Available Exploits

No exploits available for this CVE.

Related News

No news articles found for this CVE.

Affected Products

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed CRITICAL

TorchServe Server-Side Request Forgery vulnerability

GHSA-8fxr-qfr9-p34w

Advisory Details

## 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 [[email protected]](mailto:[email protected]). Please do not create a public GitHub issue.

Affected Packages

PyPI torchserve
ECOSYSTEM: ≥0.1.0 <0.8.2

CVSS Scoring

CVSS Score

9.0

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Advisory provided by GitHub Security Advisory Database. Published: October 2, 2023, Modified: November 1, 2023

References

Published: 2023-09-28T22:10:09.497Z
Last Modified: 2025-02-13T17:13:27.341Z
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