pkg:Debian/pytorch

All known vulnerabilities

• CRITICAL9.8CVE-2025-32434pytorch - security update
  from 0, < 1.7.1-7+deb11u1
• CRITICAL9.8CVE-2025-32434pytorch - security update
  from 0, < 1.7.1-7+deb11u1
• CRITICAL9.8CVE-2024-48063In PyTorch <=2.4.1, the RemoteModule has Deserialization RCE.
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• CRITICAL9.8PyTorch vulnerable to arbitrary code execution
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• HIGH8.8PyTorch Vulnerable to Remote Code Execution via Untrusted Checkpoint Files
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• HIGH7.8PyTorch pt2 Loading deserialization
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• HIGH7.8Pytorch use-after-free vulnerability
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• HIGH7.5An 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…
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• HIGH7.5A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tens…
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• HIGH7.5A 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 Ser…
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• HIGH7.5A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
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• HIGH7.5pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
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• HIGH7.5An 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 ope…
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• HIGH7.5PyTorch Tuple Handler is Vulnerable to Memory Corruption through Manipulation of None Argument
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• HIGH7.5PyTorch heap buffer overflow vulnerability
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• MEDIUM5.5PyTorch torch.jit.jit_module_from_flatbuffer memory corruption
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• MEDIUM5.5Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.
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• MEDIUM5.3pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
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• MEDIUM5.3PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU imp…
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• MEDIUM5.3In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
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• MEDIUM5.3In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
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• MEDIUM5.3In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
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• MEDIUM5.3In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
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• MEDIUM5.3PyTorch is vulnerable to memory corruption through its torch.lstm_cell function
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• MEDIUM5.3PyTorch is vulnerable to memory corruption through its torch.jit.script function
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• MEDIUM5.3PyTorch is vulnerable to memory corruption through its unpack_sequence function
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• MEDIUM5.3PyTorch is Vulnerable to Memory Consumption through pad_packed_sequence Function
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• LOW3.3An issue was discovered in PyTorch v2.5 and v2.7.1.
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• LOW3.3PyTorch Improper Resource Shutdown or Release vulnerability
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• LOW3.3PyTorch susceptible to local Denial of Service
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• LOW2.5PyTorch: Manipulation of the argument scale/zero_point leads to improper initialization via Quantized Sigmoid Module
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• —A vulnerability was found in PyTorch 2.6.0+cu124.
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• —PyTorch CUDACachingAllocator.cpp torch.cuda.memory.caching_allocator_delete memory corruption
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• —A vulnerability in the PyTorch's torch.distributed.rpc framework, specifically in versions prior to 2.2.2, allows for remote code execution…
  from 0