PyTorch bernoulli_p decompose Data Compromise

The vulnerability of the bernoullip function in the PyTorch machine learning framework’s decompose method is related to inefficient calculations by the central processor. Exploiting this vulnerability could allow a malicious actor to compromise the confidentiality of the protected information...

EUVD-2025-31108

Malicious code in bioql PyPI...

PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True.

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SUSE CVE-2025-46153

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...

Unspecified Vulnerability in PyTorch (CNVD-2025-23286)

PyTorch is a Python package open-sourced by PyTorch. PyTorch suffers from a security vulnerability that stems from an inconsistency between the bernoullip decomposition function and the CPU implementation, no details of the vulnerability are provided at this time...

PYSEC-2025-202

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...

PYSEC-2025-202

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...

Inefficient CPU Computation

Overview Affected versions of this package are vulnerable to Inefficient CPU Computation due to inconsistent behavior in the bernoullip 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 b...

Inefficient CPU Computation

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 bernoullip function when used in RNG nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d. An...

CVE-2025-46153

The PyTorch machine learning framework (versions before 3.7.0 ) contains a vulnerability in the bernoulli_p decompose function located in decompositions.py . The issue stems from a lack of full consistency with the eager CPU implementation, which negatively impacts nn.Dropout1d , nn.Dropout2d , a...

CVE-2025-46153: Inefficient CPU Computation

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...

CVE-2025-46153

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...

CVE-2025-46153

PyTorch before 3.7.0 has a bernoullip decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallbackrandom=True...