Intel Optimization For Tensorflow vulnerabilities

CVE-2022-35964 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `BlockLSTMGradV2` TensorFlow vulnerable to segfault in `BlockLSTMGradV2` ### 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(

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-2022-36004 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `tf.random.gamma` TensorFlow vulnerable to `CHECK` fail in `tf.random.gamma` ### Impact When `tf.random.gamma` 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), dtype=tf.float64, maxval=None) arg_2=tf.random.uniform(shape=(4, 4, 4,

CVE-2022-35992 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `TensorListFromTensor` TensorFlow vulnerable to `CHECK` fail in `TensorListFromTensor` ### 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=6

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-2022-36001 [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-2022-36002 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `Unbatch` TensorFlow vulnerable to `CHECK` fail in `Unbatch` ### 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.

CVE-2022-35934 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failure in tf.reshape via overflows TensorFlow vulnerable to `CHECK` failure in tf.reshape via overflows ### 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 anot

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-2022-36003 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2` TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2` ### 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

CVE-2022-35990 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient` TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient` ### 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, max

CVE-2022-36018 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `RaggedTensorToVariant` TensorFlow vulnerable to `CHECK` fail in `RaggedTensorToVariant` ### Impact If `RaggedTensorToVariant` is given a `rt_nested_splits` list that contains tensors of ranks 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 batched_input = True rt_nested_splits = tf.constant([0,32,64], shape=[3], dtype=tf.int64) rt_dens

CVE-2022-35965 [MEDIUM] CWE-476 TensorFlow vulnerable to segfault in `LowerBound` and `UpperBound` TensorFlow vulnerable to segfault in `LowerBound` and `UpperBound` ### 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.co

CVE-2023-25663 [HIGH] CWE-476 TensorFlow has Null Pointer Error in TensorArrayConcatV2 TensorFlow has Null Pointer Error in TensorArrayConcatV2 ### Impact When ctx->step_containter() is a null ptr, the Lookup function will be executed with a null pointer. ```python import tensorflow as tf tf.raw_ops.TensorArrayConcatV2(handle=['a', 'b'], flow_in = 0.1, dtype=tf.int32, element_shape_except0=1) ``` ### Patches We have patched the issue in GitHub commit [239139d2ae6a81ae9ba499ad78b56d9b2931538a](

CVE-2023-25674 [HIGH] CWE-476 TensorFlow has Null Pointer Error in RandomShuffle with XLA enable TensorFlow has Null Pointer Error in RandomShuffle with XLA enable ### Impact NPE in RandomShuffle with XLA enable ```python import tensorflow as tf func = tf.raw_ops.RandomShuffle para = {'value': 1e+20, 'seed': -4294967297, 'seed2': -2147483649} @tf.function(jit_compile=True) def test(): y = func(**para) return y test() ``` ### Patches We have patched the issue in GitHub commit [728113a3be690f

CVE-2023-25658 [HIGH] CWE-125 TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad ### Impact Out of bounds read in GRUBlockCellGrad ```python func = tf.raw_ops.GRUBlockCellGrad para = {'x': [[21.1, 156.2], [83.3, 115.4]], 'h_prev': array([[136.5], [136.6]]), 'w_ru': array([[26.7, 0.8], [47.9, 26.1], [26.2, 26.3]]), 'w_c': array([[ 0.4], [31.5], [ 0.6]]), 'b_ru': array([0.1, 0.2 ], dtype=float32), 'b_c': 0x41414141, 'r'

CVE-2022-41883 [MEDIUM] CWE-125 Out of bounds segmentation fault due to unequal op inputs in Tensorflow Out of bounds segmentation fault due to unequal op inputs in Tensorflow ### Impact [`tf.raw_ops.DynamicStitch`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/dynamic_stitch_op.cc) specifies input sizes when it is [registered](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/data_flow_ops.cc). ```cpp REGISTER_OP("DynamicStitch") .Input("in

CVE-2021-29530 [LOW] CWE-476 Invalid validation in `SparseMatrixSparseCholesky` Invalid validation in `SparseMatrixSparseCholesky` ### Impact An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`: ```python import tensorflow as tf import numpy as np from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops indices_array = np.array([[0, 0]]) value_array = np.array([-10.0], dtype=np.float32) dense_shape =

CVE-2021-29583 [LOW] CWE-125 Heap buffer overflow and undefined behavior in `FusedBatchNorm` Heap buffer overflow and undefined behavior in `FusedBatchNorm` ### Impact The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf x = tf.zeros([10, 10, 10, 6], dtype=tf.float32) scale = tf.constant([0.0], shape=[1], dtype=tf.float32) offset = tf.constant([0.0], shape=[1], dtype=tf.float32) mean = tf.constant([0.0], shape=[1], dtype=