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CVE-2025-55551, CVE-2025-55552, CVE-2025-55553, CVE-2025-55554, CVE-2025-55556, CVE-2025-55557, CVE-2025-55558, CVE-2025-55559, CVE-2025-55560
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| [CVE ID] | |
| CVE-2025-55551 | |
| [Description] | |
| 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. | |
| [Additional Information] | |
| This issue was labeled as high priority by pytorch community. | |
| [VulnerabilityType Other] | |
| Type Error | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.8.0 | |
| [Affected Component] | |
| a PyTorch API: `torch.linalg.lu` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.linalg.lu` with inductor, which can result in a system crash, causing Denial of Service. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151401 | |
| [CVE ID] | |
| CVE-2025-55552 | |
| [Description] | |
| pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together. | |
| [Additional Information] | |
| This issue was labeled as high priority by the PyTorch community. | |
| [VulnerabilityType Other] | |
| Incorrect calculation | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - version - <=2.8.0 | |
| [Affected Component] | |
| a combination of PyTorch APIs: `torch.rot90` and `torch.randn_like` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [CVE Impact Other] | |
| Silent Incorrectness | |
| [Attack Vectors] | |
| The hacker requested to compile the pytorch model consisting of `torch.rot90` and `torch.randn_like` with inductor, which can result in incorrect output results. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/147847 | |
| [CVE ID] | |
| CVE-2025-55553 | |
| [Description] | |
| A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS). | |
| [Additional Information] | |
| This issue was labeled as high priority by PyTorch community. This vulnerability is fixed in the latest PyTorch version (https://github.com/pytorch/pytorch/pull/154645) | |
| [VulnerabilityType Other] | |
| Syntax Error | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.7.0 | |
| [Affected Component] | |
| a PyTorch API: `torch.Tensor.random_()` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.Tensor.random_()` with inductor, which can result in a Syntax Error, causing Denial of Service. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151432 | |
| https://github.com/pytorch/pytorch/pull/154645 | |
| [CVE ID] | |
| CVE-2025-55554 | |
| [Description] | |
| pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long(). | |
| [Vulnerability Type] | |
| Integer Overflow | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.8.0 | |
| [Affected Component] | |
| a combination of PyTorch APIs: `torch.nan_to_num` and `.long()` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [CVE Impact Other] | |
| Silent Incorrectness | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.nan_to_num` and `.long()` with inductor, which can result in Integer Overflow when meeting `inf` input, causing Incorrect calculation. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151510 | |
| [CVE ID] | |
| CVE-2025-55556 | |
| [Description] | |
| TensorFlow v2.18.0 was discovered to output random results when compiling Embedding, leading to unexpected behavior in the application. | |
| [Additional Information] | |
| TensorFlow community has confirmed this vulnerability. | |
| [Vulnerability Type Other] | |
| Incorrect calculation | |
| [Vendor of Product] | |
| https://github.com/tensorflow/tensorflow | |
| [Affected Product Code Base] | |
| TensorFlow - version - <=2.18.0 | |
| [Affected Component] | |
| a TensorFlow API: `tf.keras.layers.Embedding` | |
| TensorFlow compiler: XLA (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/tf2xla) | |
| [Attack Type] | |
| Remote | |
| [CVE Impact Other] | |
| Silent Incorrectness | |
| [Attack Vectors] | |
| The hacker requested to compile the TensorFlow model consisting of `tf.keras.layers.Embedding` with XLA, which can result in silent incorrectness, causing the TensorFlow model to make wrong or dangerous decisions. | |
| [Reference] | |
| https://github.com/tensorflow/tensorflow/issues/82317 | |
| [CVE ID] | |
| CVE-2025-55557 | |
| [Description] | |
| 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). | |
| [Additional Information] | |
| This issue was labeled as high priority by pytorch community. This vulnerability is fixed in the latest pytorch version (https://github.com/pytorch/pytorch/pull/151931) | |
| [Vulnerability Type Other] | |
| Name Error | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.7.0 | |
| [Affected Component] | |
| a PyTorch API: `torch.cummin` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.cummin` with inductor, which can result in Name Error, causing Denial of Service. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151738 | |
| https://github.com/pytorch/pytorch/pull/151931 | |
| [CVE ID] | |
| CVE-2025-55558 | |
| [Description] | |
| 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 Denial of Service (DoS). | |
| [Additional Information] | |
| This vulnerability is fixed in the latest pytorch version (https://github.com/pytorch/pytorch/pull/151887) | |
| [Vulnerability Type] | |
| Buffer Overflow | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.7.0 | |
| [Affected Component] | |
| a combination of PyTorch APIs: `torch.nn.Conv2d`, `torch.nn.functional.hardshrink` and `torch.Tensor.view-torch.mv()` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.nn.Conv2d`, `torch.nn.functional.hardshrink`, and `torch.Tensor.view-torch.mv()` with inductor, which can result in Buffer Overflow, causing Denial of Service. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151523 | |
| https://github.com/pytorch/pytorch/pull/151887 | |
| [CVE ID] | |
| CVE-2025-55559 | |
| [Description] | |
| An issue was discovered TensorFlow v2.18.0. A Denial of Service (DoS) occurs when padding is set to 'valid' in tf.keras.layers.Conv2D. | |
| [Additional Information] | |
| TensorFlow community has confirmed this vulnerability. | |
| [Vulnerability Type Other] | |
| Runtime Error | |
| [Vendor of Product] | |
| https://github.com/tensorflow/tensorflow | |
| [Affected Product Code Base] | |
| TensorFlow - version - <=2.18.0 | |
| [Affected Component] | |
| a TensorFlow API: `tf.keras.layers.Conv2D` | |
| TensorFlow compiler: XLA (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/tf2xla) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to change the parameter `padding='valid'` of `tf.keras.layers.Conv2D`, which is compiled by XLA. In such a case, `tf.keras.layers.Conv2D` will throw a runtime error because it receives a negative dimension size. | |
| [Reference] | |
| https://github.com/tensorflow/tensorflow/issues/84205 | |
| [CVE ID] | |
| CVE-2025-55560 | |
| [Description] | |
| 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. | |
| [Additional Information] | |
| This issue was labeled as high priority by pytorch community. This vulnerability is fixed in the latest pytorch version (https://github.com/pytorch/pytorch/pull/151897) | |
| [Vulnerability Type] | |
| Not Implemented Error | |
| [Vendor of Product] | |
| https://github.com/pytorch/pytorch | |
| [Affected Product Code Base] | |
| pytorch - <=2.7.0 | |
| [Affected Component] | |
| a combination of PyTorch APIs: `torch.Tensor.to_sparse()` and `torch.Tensor.to_dense()` | |
| PyTorch compiler: Inductor (https://github.com/pytorch/pytorch/tree/main/torch/_inductor) | |
| [Attack Type] | |
| Remote | |
| [Impact Denial of Service] | |
| true | |
| [Attack Vectors] | |
| The hacker requested to compile the PyTorch model consisting of `torch.Tensor.to_sparse()` and `torch.Tensor.to_dense()` with inductor, which can result in Not Implemented Error, causing Denial of Service. | |
| [Reference] | |
| https://github.com/pytorch/pytorch/issues/151522 | |
| https://github.com/pytorch/pytorch/pull/151897 |
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