Inefficient CPU Computation Affecting torch package, versions [2.6.0,2.7.0)
Threat Intelligence
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-TORCH-13052812
- published26 Sept 2025
- disclosed25 Sept 2025
- creditShaoyu Yang
Introduced: 25 Sep 2025
NewCVE-2025-46153 (opens in a new tab)How to fix?
Upgrade torch to version 2.7.0 or higher.
Overview
torch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration
Affected versions of this package are vulnerable to Inefficient CPU Computation due to inconsistent behavior in the bernoulli_p() function when used in RNG nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d. An attacker can cause unintended or incorrect dropout behavior in neural network layers by triggering fallback to the affected implementation when fallback_random=True.