GHSA-8fxr-qfr9-p34w is a critical-severity (CVSS 9.8) Server-Side Request Forgery (SSRF) vulnerability in torchserve. 5 public exploit references exist, so weaponization risk is real. A fix is available for torchserve — see the affected versions and patch details below.
TorchServe Server-Side Request Forgery vulnerability
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for GHSA-8fxr-qfr9-p34w.
EPSS Exploitation Probability
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.
How urgent is this, really
GHSA-8fxr-qfr9-p34w plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 377,166 CVEs with a current EPSS score, this one falls in the 10–50% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
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
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
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | torchserve | ≥ 0.1.0&&< 0.8.2 | 0.8.2pip install --upgrade 'torchserve==0.8.2' |
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…
TorchServe is a tool for serving and scaling PyTorch models in productio…
TorchServe is a tool for serving and scaling PyTorch models in productio…
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-8fxr-qfr9-p34w 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-8fxr-qfr9-p34w can be triaged on real exposure rather than presence alone.
Tailored to GHSA-8fxr-qfr9-p34w. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
How to detect GHSA-8fxr-qfr9-p34w
A community-maintained Nuclei template exists for this CVE. You can scan for it directly:
nuclei -id ghsa-8fxr-qfr9-p34w -u https://target- Template
- PyTorch TorchServe SSRF
- Severity
- critical
- Impact
- Unauthenticated attackers can load malicious models from arbitrary URLs and write files to disk due to lack of input validation, potentially compromising the PyTorch TorchServe system and accessing internal network resources through SSRF.
- Remediation
- Update PyTorch TorchServe to version 0.8.2 or later that includes allowed_urls validation and restricts model loading to trusted sources.
Template by ProjectDiscovery nuclei-templates (DhiyaneshDk), MIT licensed. View the full template. Scan only systems you are authorised to test.
Frequently Asked Questions
Is GHSA-8fxr-qfr9-p34w in your dependencies?
O3 Security finds GHSA-8fxr-qfr9-p34w across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.