1832 matches found
Security Bulletin: IBM watsonx Orchestrate Developer Edition affected by vulnerability in torch-2.6.0-cp313-cp313-manylinux1_x86_64.whl
Summary Security Bulletin: IBM watsonx Orchestrate Developer Edition affected by vulnerability in torch-2.6.0-cp313-cp313-manylinux1x8664.whl Vulnerability Details CVEID:CVE-2025-4287 DESCRIPTION: A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this...
Security Bulletin: IBM watsonx Orchestrate Developer Edition affected by vulnerability in torch-2.6.0-cp313-cp313-manylinux1_x86_64.whl
Summary Security Bulletin: IBM watsonx Orchestrate Developer Edition affected by vulnerability in torch-2.6.0-cp313-cp313-manylinux1x8664.whl Vulnerability Details CVEID:CVE-2025-3000 DESCRIPTION: A vulnerability classified as critical has been found in PyTorch 2.6.0. This affects the function...
Improper Input Validation
picklescan is vulnerable to Improper Input Validation. The vulnerability is due to inadequate validation in the scanning logic that fails to properly inspect pickle files with PyTorch-related extensions, which allows an attacker to bypass security checks and execute malicious code when the file i...
The vulnerability of the `torch.nn.Fold` module in the PyTorch machine learning framework allows a hacker to trigger a denial-of-service attack.
The vulnerability of the torch.nn.Fold module in the PyTorch machine learning framework is related to buffer overflow attacks. Exploiting this vulnerability could allow a malicious actor to cause a service failure...
The vulnerability of the bernoulli_p function in the decompose() function of the PyTorch machine learning framework allows attackers to compromise the confidentiality of the protected information.
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...
The vulnerability of the PyTorch machine learning framework’s `torch.nn.PairwiseDistance(p=2)` configuration allows attackers to compromise the confidentiality of the protected information.
The vulnerability of the torch.nn.PairwiseDistancep=2 configuration in the PyTorch machine learning framework is related to insufficient validation of input data. Exploiting this vulnerability could allow an attacker to compromise the confidentiality of the protected information...
The vulnerability of the `torch.nn.functional.max_pool2d()` function in the PyTorch machine learning framework allows a hacker to execute arbitrary code.
The vulnerability of the torch.nn.functional.maxpool2d function in the PyTorch machine learning framework is related to the execution of operations outside of the buffer boundaries in memory. Exploiting this vulnerability could allow a malicious actor to execute arbitrary code...
The vulnerability of the `torch.bitwise_right_shift` interface in the PyTorch machine learning framework allows attackers to compromise the accessibility of protected information.
The vulnerability of the torch.bitwiserightshift interface in the PyTorch machine learning framework is related to the execution of the operation outside the buffer in memory. Exploiting this vulnerability could allow an attacker to compromise the accessibility of protected information...
Insecure Deserialization
monai is vulnerable to Insecure Deserialization. The vulnerability is due to loading of untrusted checkpoint files like torch.load used without safe guards. This allows an attacker to supply a crafted checkpoint that executes arbitrary code during deserialization...
BIT-PYTORCH-2025-55560
An issue in pytorch v2.7.0 can lead to a Denial of Service DoS when a PyTorch model consists of torch.Tensor.tosparse and torch.Tensor.todense and is compiled by Inductor...
The vulnerability of the torch.nan_to_num() function in the PyTorch machine learning framework allows a attacker to trigger a service failure.
The vulnerability of the torch.nantonum function in the PyTorch machine learning framework is related to integer overflow. Exploiting this vulnerability could allow a malicious actor to trigger a service failure...
The vulnerability of the torch.cummin interface in the PyTorch machine learning framework allows a attacker to induce a service failure.
The vulnerability of the torch.cummin interface in the PyTorch machine learning framework is related to insufficient exception handling. Exploiting this vulnerability could allow a malicious actor to cause service failures...
The vulnerability of the `torch.Tensor.to_sparse()` and `torch.Tensor.to_dense()` methods in the PyTorch machine learning framework allows attackers to trigger a denial-of-service attack.
The vulnerability of the torch.Tensor.tosparse and torch.Tensor.todense methods in the PyTorch machine learning framework is related to uncontrolled resource consumption. Exploiting this vulnerability could allow a malicious actor to cause service failures...
Vulnerability of the torch.rot90() and torch.randn_like() functions in the PyTorch machine learning framework, which can be exploited by attackers to cause service failures.
The vulnerability of the torch.rot90 and torch.randnlike functions in the PyTorch machine learning framework is related to incorrect calculations. Exploiting this vulnerability may allow a malicious actor to cause service failures...
The vulnerability of the `torch.Tensor.random_()` method in the PyTorch machine learning framework, which allows a hacker to trigger a denial-of-service attack.
The vulnerability of the torch.Tensor.random method in the PyTorch machine learning framework is related to insufficient exception handling. Exploiting this vulnerability could allow a malicious actor to cause service failures...
Vulnerabilities of methods like torch.nn.Conv2d(), torch.nn.functional.hardshrink(), torch.Tensor.view(), and torch.Tensor.mv() in the PyTorch machine learning framework, which allow attackers to trigger a denial-of-service attack.
The vulnerabilities of the methods torch.nn.Conv2d, torch.nn.functional.hardshrink, torch.Tensor.view, and torch.Tensor.mv in the PyTorch machine learning framework are related to uncontrolled resource consumption. Exploiting these vulnerabilities could allow a malicious actor to cause service...
The vulnerability of the torch.linalg.lu() function in the PyTorch machine learning framework allows a attacker to cause a service failure.
The vulnerability of the torch.linalg.lu function in the PyTorch machine learning framework is related to unlimited resource consumption. Exploiting this vulnerability could allow a malicious actor to cause service failures...
EUVD-2025-33343
scio is vunerable to Remote Command Execution through PyTorch...