# GHSA-4mqg-h5jf-j9m7: TorchServe Pre-Auth Remote Code Execution

> Attackers can execute arbitrary code on TorchServe servers via unsafe deserialization in the SnakeYAML library without authentication.

- **Severity:** Not scored
- **Exploited in the wild:** Not known
- **Login needed:** Not needed
- **Attackable:** Over the internet
- **Victim action needed:** Not needed
- **Fix available:** Yes
- **Published:** 2 Oct 2023

## What is GHSA-4mqg-h5jf-j9m7?

GHSA-4mqg-h5jf-j9m7 is an unsafe deserialization vulnerability in torchserve, published on 2 Oct 2023. An attacker can exploit it over the internet without logging in, with no action from the victim.

### Official 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[)](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.

## Which versions are affected by GHSA-4mqg-h5jf-j9m7?

2 open source packages ship an affected version, including torchserve.


| Package | Ecosystem | Affected | Fixed in |
| --- | --- | --- | --- |
| `torchserve` | PyPI | From 0.3.0 | 0.8.2 |
| `torchserve` | PyPI | From 0.3.0 | 0.8.2 |

## How do I fix GHSA-4mqg-h5jf-j9m7?

Upgrade torchserve to 0.8.2 or later.

## Is GHSA-4mqg-h5jf-j9m7 being exploited?

There is no known exploitation of GHSA-4mqg-h5jf-j9m7 in the wild and no public exploit code.

## Fix prompt for AI coding agents

```text
Check this codebase for GHSA-4mqg-h5jf-j9m7 and fix it if it is affected.

What it is: GHSA-4mqg-h5jf-j9m7 is an unsafe deserialization vulnerability in torchserve, published on 2 Oct 2023. An attacker can exploit it over the internet without logging in, with no action from the victim.
Exploitation: There is no known exploitation of GHSA-4mqg-h5jf-j9m7 in the wild and no public exploit code.

Affected packages and fixed versions:
- torchserve (PyPI): affected from 0.3.0; fixed in 0.8.2
- torchserve (PyPI): affected from 0.3.0; fixed in 0.8.2

Task:
1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.
2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.
3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.
4. Run the tests and summarise what changed and why.
5. If nothing in the project is affected, say so and change nothing.

Reference: https://insights.sbomify.com/GHSA-4mqg-h5jf-j9m7/ (Markdown: https://insights.sbomify.com/GHSA-4mqg-h5jf-j9m7/index.md)
```

## Sources

- OSV: https://osv.dev/vulnerability/GHSA-4mqg-h5jf-j9m7

---
From sbomify Insights: https://insights.sbomify.com/GHSA-4mqg-h5jf-j9m7/
Summaries are generated and may contain mistakes; the official description above is the record.