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MEDIUM severity

PyTorch is vulnerable to memory corruption through its torch.lstm_cell functionGHSA-qfhq-4f3w-5fph

MEDIUMFix: pytorch/pytorch@999d94b

GHSA-qfhq-4f3w-5fph is a medium-severity (CVSS 5.3) Buffer Overflow vulnerability in torch. A fix is available for torch — see the affected versions and patch details below.

Also known asBIT-pytorch-2025-3001CVE-2025-3001PYSEC-2025-195
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 9, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

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.

Exploitation and automatability from CISA’s SSVC triage for GHSA-qfhq-4f3w-5fph.

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs10th percentile — riskier than 10% of all scored CVEsHighest risk
0.00%0.23%0.47%0.70%0.2%0.2%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

1 pkg affected
🐍torch

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

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstm_cell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used.

A patch is available through commit 999d94b.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItorchall versions2.10.0pip install --upgrade 'torch==2.10.0'

Affected Products

1 product · 1 configurations
Application
pytorchlinuxfoundation
1 version
2.6.0

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for torch, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update torch to 2.10.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qfhq-4f3w-5fph is resolved across your whole dependency graph.

  3. Workarounds

    Constrain what reaches the vulnerable code: limit the size and shape of untrusted input, isolate the affected component in a sandboxed or least-privileged process, and enable the platform's memory-safety mitigations (ASLR, stack protector, hardened allocator) so an out-of-bounds access is more likely to fail closed than to be exploitable.

Frequently Asked Questions

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstm_cell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used. A patch is available through commit [999d94b](https://github.com/pytorch/pytorch/commit/999d94b5ede5f4ec111ba7dd144129e2c2725b03).
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PyTorch is vulnerable to memory corruption through its…