How severe is GHSA-8fxr-qfr9-p34w?

GHSA-8fxr-qfr9-p34w has a CVSS score of 9.8/10, rated CRITICAL. Immediate patching is strongly recommended.

Which packages are affected by GHSA-8fxr-qfr9-p34w?

GHSA-8fxr-qfr9-p34w affects the following packages: torchserve (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-8fxr-qfr9-p34w?

Update torchserve to version 0.8.2 or later. Run your dependency manager's update command and verify no transitive dependencies pin the vulnerable version. Use O3 Security's SCA scanner to confirm the fix is applied across your entire dependency tree.

Is GHSA-8fxr-qfr9-p34w actively exploited in the wild?

Yes. There are 4 known exploit references for GHSA-8fxr-qfr9-p34w, including 3 in-the-wild exploitations observed on GitHub and other sources. Treat this as actively exploitable and prioritize patching immediately. All exploit code should only be run in an isolated sandbox environment for research or authorized testing — never against production systems without explicit written authorization.

What is the EPSS score for GHSA-8fxr-qfr9-p34w?

GHSA-8fxr-qfr9-p34w has an EPSS (Exploit Prediction Scoring System) score of 91.0%, placing it in the 100th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. A score above 50% indicates significantly elevated exploitation risk — immediate patching is strongly recommended.

What type of vulnerability is GHSA-8fxr-qfr9-p34w?

GHSA-8fxr-qfr9-p34w is classified as Server-Side Request Forgery (SSRF) (CWE-918). This weakness type describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation. This is a high-impact weakness class that often enables remote code execution or data exposure.

Is there exploit tooling available for GHSA-8fxr-qfr9-p34w?

A Nuclei detection template exists for GHSA-8fxr-qfr9-p34w, meaning automated scanners can fingerprint it. Public exploit tooling significantly increases real-world risk — prioritize patching and ensure detection coverage. Exploit tooling should only ever be used in isolated, authorized testing environments.

When was GHSA-8fxr-qfr9-p34w published, and has it been updated?

GHSA-8fxr-qfr9-p34w was published on October 2, 2023 and was last updated on February 16, 2024. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

GHSA-8fxr-qfr9-p34w

CRITICAL

TorchServe Server-Side Request Forgery vulnerability

Also known asCVE-2023-43654

Published

Oct 2, 2023

Updated

Feb 16, 2024

Affected

1 pkg

Patched

1 / 1

Exploits

4 known

EPSS Exploitation Probability

via FIRST.org ↗

91.0%probability of exploitation in next 30 days

Very High Risk100th percentile-0.66%

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Blast Radius

1 pkg affected

🐍torchserve

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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.

Affected Packages

1 total 1 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`torchserve`	≥ 0.1.0&&< 0.8.2	0.8.2

Exploits & PoCs

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

TorchServe is a tool for serving and scaling PyTorch models in productio…

github.comGithubOct 2023

TorchServe is a tool for serving and scaling PyTorch models in productio…

github.comMetasploitOct 2023

TorchServe is a tool for serving and scaling PyTorch models in productio…

packetstormsecurity.comNVDOct 2023

Frequently Asked Questions

## 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/

O3 Security · Impact-Aware SCA

Is GHSA-8fxr-qfr9-p34w in your stack?

O3 detects GHSA-8fxr-qfr9-p34w across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

Scan my stack How O3 SCA works

CVSS Score

9.8/10

CVSS v3 base score

Vector breakdown

AV:Attack Vector

Network

AC:Attack Complexity

Low

PR:Privileges Required

None

UI:User Interaction