#dos

### Impact If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf num_bits = 8 narrow_range = False inputs = tf.constant(0, shape=[4], dtype=tf.float32) min = tf.constant([], shape=[4,0,0], dtype=tf.float32) max = tf.constant(0, shape=[4], dtype=tf.float32) tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range) ``` ### Patches We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guid...

### Impact The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor: ```python import tensorflow as tf tf.reshape(tensor=[[1]],shape=tf.constant([0 for i in range(255)], dtype=tf.int64)) ``` This is another instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (https://github.com/advisories/GHSA-prcg-wp5q-rv7p). ### Patches We have patched the issue in GitHub commit [61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555](https://github.com/tensorflow/tensorflow/commit/61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/te...

### Impact The implementation of SobolSampleOp is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by assuming `input(0)`, `input(1)`, and `input(2)` to be scalar. ```python import tensorflow as tf tf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1])) ``` ### Patches We have patched the issue in GitHub commit [c65c67f88ad770662e8f191269a907bf2b94b1bf](https://github.com/tensorflow/tensorflow/commit/c65c67f88ad770662e8f191269a907bf2b94b1bf). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been r...

indy-node is the server portion of Hyperledger Indy, a distributed ledger purpose-built for decentralized identity. ### Impact An attacker can max out the number of client connections allowed by the ledger that was deployed using guidance provided in the indy-node repository, leaving the ledger unable to be used for its intended purpose. The ledger content will not be impacted by the attack, and the ledger will resume servicing valid client requests after the attack. ### Mitigations This attack exploits the trade-off between resilience and availability. Any protection against abusive client connections will also prevent the network being accessed by certain legitimate users. As a result, validator nodes must tune their firewall rules to ensure the right trade-off for their network's expected users. The guidance previously provided enabled a low-cost DDoS attack. The [guidance to network operators for the use of firewall rules](https://github.com/hyperledger/indy-node/blob/main/do...

TensorFlow is an open source platform for machine learning. The implementation of SobolSampleOp is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by assuming `input(0)`, `input(1)`, and `input(2)` to be scalar. This issue has been patched in GitHub commit c65c67f88ad770662e8f191269a907bf2b94b1bf. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This issue has been patched in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

TensorFlow is an open source platform for machine learning. The implementation of `AvgPool3DGradOp` does not fully validate the input `orig_input_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 9178ac9d6389bdc54638ab913ea0e419234d14eb. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

### Impact If `tf.sparse.cross` receives an input `separator` that is not a scalar, it gives a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf tf.sparse.cross(inputs=[],name='a',separator=tf.constant(['a', 'b'],dtype=tf.string)) ``` ### Patches We have patched the issue in GitHub commit [83dcb4dbfa094e33db084e97c4d0531a559e0ebf](https://github.com/tensorflow/tensorflow/commit/83dcb4dbfa094e33db084e97c4d0531a559e0ebf). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Kang Hong Jin.

### Impact When `Conv2DBackpropInput` receives empty `out_backprop` inputs (e.g. `[3, 1, 0, 1]`), the current CPU/GPU kernels `CHECK` fail (one with dnnl, the other with cudnn). This can be used to trigger a denial of service attack. ```python import tensorflow as tf import numpy as np input_sizes = [3, 1, 1, 2] filter = np.ones([1, 3, 2, 3]) out_backprop = np.ones([3, 1, 0, 3]) strides = [1, 1, 2, 1] padding = 'VALID' tf.raw_ops.Conv2DBackpropInput( input_sizes = input_sizes, filter = filter, out_backprop = out_backprop, strides = strides, padding = padding ) ``` ### Patches We have patched the issue in GitHub commit [27a65a43cf763897fecfa5cdb5cc653fc5dd0346](https://github.com/tensorflow/tensorflow/commit/27a65a43cf763897fecfa5cdb5cc653fc5dd0346). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more inform...