PYSEC-2025-198

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

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...

PYSEC-2025-198

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

DEBIAN-CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

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...

UBUNTU-CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

UBUNTU-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-2025-46148

The PyTorch machine learning framework is vulnerable in versions through 2.6.0 due to insufficient validation of input data within the torch.nn.PairwiseDistance(p=2) configuration. When using eager mode , this flaw causes the function to produce incorrect results , which may allow an attacker to ...

Reachable Assertion

Overview torch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration Affected versions of this package are vulnerable to Reachable Assertion in the torch.linalg.lu function. In AOTAutograd mode LU decomposition can't accept slice operation and An attacker can cause the...

Reachable Assertion

Overview Affected versions of this package are vulnerable to Reachable Assertion in the torch.linalg.lu function. In AOTAutograd mode LU decomposition can't accept slice operation and An attacker can cause the application to become unresponsive or crash if backend="aoteager" by providing speciall...

PyTorch 安全漏洞

PyTorch is a Python package open-sourced by PyTorch. PyTorch suffers from an information disclosure vulnerability that stems from nn.PairwiseDistancep=2 producing incorrect results in eager mode, no details of the vulnerability are provided at this time...

CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

CVE-2025-46153

The PyTorch machine learning framework (versions before 3.7.0 ) contains a security vulnerability in the bernoulli_p function within decompositions.py. The root cause is an inconsistency between this decomposition function and the eager CPU implementation, which negatively impacts nn.Dropout1d, n...

CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

CVE-2025-46148: Undefined Security Weakness

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

CVE-2025-46148

In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistancep=2 produces incorrect results...

PT-2025-39383

Name of the Vulnerable Software and Affected Versions PyTorch versions prior to 3.7.0 Description The software contains an inconsistency in the bernoulli p decompose function within decompositions.py. This function does not fully align with the eager CPU implementation, which impacts the...

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...