69 matches found
SUSE CVE-2025-3001
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...
PyTorch is vulnerable to memory corruption through its torch.lstm_cell function
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used. A patch is available...
PYSEC-2025-195
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...
PYSEC-2025-195
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...
AZL-73180 CVE-2025-3001 affecting package pytorch for versions less than 2.2.2-10
A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...
Out-of-bounds Write
Overview torch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration Affected versions of this package are vulnerable to Out-of-bounds Write due to the torch.lstmcell function. An attacker can corrupt memory by manipulating the function's input. Note: This is only...
Out-of-bounds Write
Overview Affected versions of this package are vulnerable to Out-of-bounds Write due to the torch.lstmcell function. An attacker can corrupt memory by manipulating the function's input. Note: This is only exploitable if the attacker has local access to the system. Remediation A fix was pushed int...
PyTorch 缓冲区错误漏洞
PyTorch is a Python package open-sourced by PyTorch. PyTorch has a buffer overflow vulnerability that stems from the failure of the function torch.lstmcell to properly validate the length size of the input data, which can be exploited by an attacker to execute arbitrary code on the system or caus...
BIT-TENSORFLOW-2020-26270 CHECK-fail in LSTM with zero-length input in TensorFlow
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...
SUSE CVE-2020-26270
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...
SUSE CVE-2022-35964
TensorFlow is an open source platform for machine learning. The implementation of BlockLSTMGradV2 does not fully validate its inputs. This results in a a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit...
GHSA-F7R5-Q7CX-H668 TensorFlow vulnerable to segfault in `BlockLSTMGradV2`
Impact The implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3 This results in a a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf usepeephole =...
GHSA-2VV3-56QG-G2CF Missing validation causes denial of service via `LSTMBlockCell`
Impact The implementation of tf.rawops.LSTMBlockCell does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.rawops.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...
PT-2022-19452
Name of the Vulnerable Software and Affected Versions TensorFlow versions prior to 2.9.0 TensorFlow versions prior to 2.8.1 TensorFlow versions prior to 2.7.2 TensorFlow versions prior to 2.6.4 Description The implementation of tf.raw ops.LSTMBlockCell does not fully validate the input arguments,...
Denial Of Service (DoS)
tensorflow is vulnerable to denial of service. A CHECK-fail in LSTM allows an attacker to crash the application with a zero-length input...
Google TensorFlow Denial of Service Vulnerability (CNVD-2021-00093)
Google TensorFlow is a suite of end-to-end open source platforms for machine learning from Google USA. Google TensorFlow suffers from a denial-of-service vulnerability that stems from the LSTM GRU layer receiving a zero-length input when using a CUDA backend, which results in a check failure. An...
CVE-2020-26270
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...
CVE-2020-26270
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...
PYSEC-2020-336
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...
PYSEC-2020-256
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...