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Wyczyść filtryPicklescan is vulnerable to RCE via missing detection when calling built-in python _operator.attrgetter
Picklescan is vulnerable to RCE via missing detection when calling built-in python _operator.methodcaller
Picklescan is vulnerable to RCE via missing detection when calling numpy.f2py.crackfortran.getlincoef
Picklescan is vulnerable to RCE via missing detection when calling numpy.f2py.crackfortran.param_eval
Picklescan is vulnerable to RCE through missing detection when calling built-in python operator.methodcaller
Picklescan missing detection when calling pty.spawn
An issue was discovered in PyTorch v2.5 and v2.7.1. Omission of profiler.stop() can cause torch.profiler.profile (PythonTracer) to crash or hang during finalization, leading to a Denial of Service (Do
Keras framework vulnerable to deserialization of untrusted data
scio is vunerable to Remote Command Execution through PyTorch
An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.
A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a
A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS).
pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation.
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,
In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
Picklescan is Vulnerable to Unsafe Globals Check Bypass through Subclass Imports
Picklescan: ZIP archive scan bypass is possible through non-exhaustive Cyclic Redundancy Check
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