GHSA-4mqg-h5jf-j9m7 is a critical-severity (CVSS 9.9) remote code execution vulnerability in torchserve. A fix is available for torchserve — see the affected versions and patch details below.
TorchServe Pre-Auth Remote Code Execution
Real-World Exposure
torchserveReal-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
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://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/pull/2523 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
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | torchserve | ≥ 0.3.0&&< 0.8.2 | 0.8.2pip install --upgrade 'torchserve==0.8.2' |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for torchserve, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update torchserve to 0.8.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-4mqg-h5jf-j9m7 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-4mqg-h5jf-j9m7 can be triaged on real exposure rather than presence alone.
Tailored to GHSA-4mqg-h5jf-j9m7. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-4mqg-h5jf-j9m7 in your dependencies?
O3 Security finds GHSA-4mqg-h5jf-j9m7 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.