Intel Optimization For Tensorflow vulnerabilities

CVE-2022-35966 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedAvgPool` TensorFlow vulnerable to segfault in `QuantizedAvgPool` ### 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([]

CVE-2022-35967 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedAdd` TensorFlow vulnerable to segfault in `QuantizedAdd` ### 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], dty

CVE-2022-35988 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank` TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank` ### 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

CVE-2022-35997 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `tf.sparse.cross` TensorFlow vulnerable to `CHECK` fail in `tf.sparse.cross` ### 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 [83d

CVE-2022-35998 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `EmptyTensorList` TensorFlow vulnerable to `CHECK` fail in `EmptyTensorList` ### 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) ``

CVE-2022-35994 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `CollectiveGather` TensorFlow vulnerable to `CHECK` fail in `CollectiveGather` ### 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_k

CVE-2022-35993 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `SetSize` TensorFlow vulnerable to `CHECK` fail in `SetSize` ### 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) ``` ### Pat

CVE-2022-36019 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel` TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel` ### 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

CVE-2022-36005 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient` TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient` ### 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

CVE-2022-35968 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad` TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad` ### Impact The implementation of `AvgPoolGrad` does not fully validate the input `orig_input_shape`. This results in a `CHECK` failure which 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 = "VALID" data_format = "NHWC" orig_input_shape = tf.constant(-536870912, shap

CVE-2022-35963 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad` TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad` ### 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(

CVE-2022-36001 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `DrawBoundingBoxes` TensorFlow vulnerable to `CHECK` fail in `DrawBoundingBoxes` ### 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.r

CVE-2022-35985 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `LRNGrad` TensorFlow vulnerable to `CHECK` fail in `LRNGrad` ### Impact If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf depth_radius = 1 bias = 1.59018219 alpha = 0.117728651 beta = 0.404427052 input_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.f

CVE-2022-35987 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `DenseBincount` TensorFlow vulnerable to `CHECK` fail in `DenseBincount` ### Impact `DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf binary_output = True input = tf.random.uniform(shape=[0, 0], minval=

CVE-2022-35995 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `AudioSummaryV2` TensorFlow vulnerable to `CHECK` fail in `AudioSummaryV2` ### Impact When `AudioSummaryV2` receives an input `sample_rate` with more than one element, it gives a `CHECK` fails that can be used to trigger a denial of service attack. ```python import tensorflow as tf arg_0='' arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None) arg_2=tf.random.uniform(shape=(2,1), dtype=tf.float32, maxval=None

CVE-2022-35983 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `Save` and `SaveSlices` TensorFlow vulnerable to `CHECK` fail in `Save` and `SaveSlices` ### Impact If `Save` or `SaveSlices` is run over tensors of an unsupported `dtype`, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf filename = tf.constant("") tensor_names = tf.constant("") # Save data = tf.cast(tf.random.uniform(shape=[1], minval=-10000, maxval=100

CVE-2022-35999 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput` TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput` ### 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,

CVE-2022-36026 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `QuantizeAndDequantizeV3` TensorFlow vulnerable to `CHECK` fail in `QuantizeAndDequantizeV3` ### Impact If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits` input tensor, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf signed_input = True range_given = False narrow_range = False axis = -1 input = tf.constant(-3.5, shape=[1], dtype=tf.float32) i

CVE-2022-35996 [MEDIUM] CWE-369 TensorFlow vulnerable to floating point exception in `Conv2D` TensorFlow vulnerable to floating point exception in `Conv2D` ### 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

CVE-2022-35984 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `ParameterizedTruncatedNormal` TensorFlow vulnerable to `CHECK` fail in `ParameterizedTruncatedNormal` ### Impact `ParameterizedTruncatedNormal` assumes `shape` is of type `int32`. A valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf seed = 1618 seed2 = 0 shape = tf.random.uniform(shape=[3], minval=-10000, maxval=