Intel Optimization For Tensorflow vulnerabilities

CVE-2022-35989 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `MaxPool` TensorFlow vulnerable to `CHECK` fail in `MaxPool` ### 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' dat

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-2022-35959 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad` TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad` ### 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 = "NDHW

CVE-2022-35971 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars` TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars` ### 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],

CVE-2022-35969 [MEDIUM] TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput` TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput` ### Impact The implementation of `Conv2DBackpropInput` requires `input_sizes` to be 4-dimensional. Otherwise, it gives a `CHECK` failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf strides = [1, 1, 1, 1] padding = "SAME" use_cudnn_on_gpu = True explicit_paddings = [] data_format = "NHWC" dilations =

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-2022-41911 [MEDIUM] CWE-704 Invalid char to bool conversion when printing a tensor Invalid char to bool conversion when printing a tensor ### Impact When [printing a tensor](https://github.com/tensorflow/tensorflow/blob/807cae8a807960fd7ac2313cde73a11fc15e7942/tensorflow/core/framework/tensor.cc#L1200-L1227), we get it's data as a `const char*` array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from `char` to `bool` are undefined if

CVE-2023-25665 [HIGH] CWE-476 TensorFlow has Null Pointer Error in SparseSparseMaximum TensorFlow has Null Pointer Error in SparseSparseMaximum ### Impact When `SparseSparseMaximum` is given invalid sparse tensors as inputs, it can give an NPE. ```python import tensorflow as tf tf.raw_ops.SparseSparseMaximum( a_indices=[[1]], a_values =[ 0.1 ], a_shape = [2], b_indices=[[]], b_values =[2 ], b_shape = [2], ) ``` ### Patches We have patched the issue in GitHub commit [5e0ecfb42f5f65629fd7a4edd6

CVE-2023-33976 [HIGH] CWE-190 TensorFlow has segfault in array_ops.upper_bound TensorFlow has segfault in array_ops.upper_bound ### Impact `array_ops.upper_bound` causes a segfault when not given a rank 2 tensor. ### Patches We have patched the issue in GitHub commit [915884fdf5df34aaedd00fc6ace33a2cfdefa586](https://github.com/tensorflow/tensorflow/commit/915884fdf5df34aaedd00fc6ace33a2cfdefa586). The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow

CVE-2023-25676 [HIGH] CWE-476 TensorFlow has null dereference on ParallelConcat with XLA TensorFlow has null dereference on ParallelConcat with XLA ### Impact When running with XLA, `tf.raw_ops.ParallelConcat` segfaults with a nullptr dereference when given a parameter `shape` with rank that is not greater than zero. ```python import tensorflow as tf func = tf.raw_ops.ParallelConcat para = {'shape': 0, 'values': [1]} @tf.function(jit_compile=True) def test(): y = func(**para) return y test(

CVE-2023-25667 [MEDIUM] CWE-190 TensorFlow vulnerable to segfault when opening multiframe gif TensorFlow vulnerable to segfault when opening multiframe gif ### Impact Integer overflow occurs when 2^31 <= num_frames * height * width * channels < 2^32, for example Full HD screencast of at least 346 frames. ```python import urllib.request dat = urllib.request.urlopen('https://raw.githubusercontent.com/tensorflow/tensorflow/1c38ad9b78ffe06076745a1ee00cec42f39ff726/tensorflow/core/lib/gif/testdata/3

CVE-2021-29612 [LOW] CWE-120 Heap buffer overflow in `BandedTriangularSolve` Heap buffer overflow in `BandedTriangularSolve` ### Impact An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`: ```python import tensorflow as tf import numpy as np matrix_array = np.array([]) matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32) rhs_array = np.array([1,1]) rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(

CVE-2021-29591 [HIGH] CWE-674 Stack overflow due to looping TFLite subgraph Stack overflow due to looping TFLite subgraph ### Impact TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the [`While` implementation](https://github.com/tensorflow/tensorflow

CVE-2021-29608 [MEDIUM] CWE-131 Heap OOB and null pointer dereference in `RaggedTensorToTensor` Heap OOB and null pointer dereference in `RaggedTensorToTensor` ### Impact Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty: ```python import tensorflow as tf shape = tf.constant([-1, -1], shape=[2], dtype=tf.int64) values = tf.constant([], shape=[0], dtype=tf.int64) default_value = tf.constant(404, dtype=tf.i

CVE-2021-29614 [MEDIUM] CWE-665 Interpreter crash from `tf.io.decode_raw` Interpreter crash from `tf.io.decode_raw` ### Impact The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. ```python import tensorflow as tf tf.io.decode_raw(tf.constant(["1","2","3","4"]), tf.uint16, fixed_length=4) ``` The [implementation of the padded version](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167

CVE-2021-41214 [HIGH] CWE-824 Reference binding to `nullptr` in `tf.ragged.cross` Reference binding to `nullptr` in `tf.ragged.cross` ### Impact The [shape inference code for `tf.ragged.cross`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/ragged_array_ops.cc#L64) has an undefined behavior due to binding a reference to `nullptr`. In the following scenario, this results in a crash: ```python import tensorflow as tf @tf.function def t

CVE-2021-29606 [MEDIUM] CWE-125 Heap OOB read in TFLite Heap OOB read in TFLite ### Impact A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of [`Split_V`](https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99): ```cc const int input_size = SizeOfDimension(input, axis_value); ``` If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the [`SizeOfDimension` f

CVE-2021-29540 [LOW] CWE-120 Heap buffer overflow in `Conv2DBackpropFilter` Heap buffer overflow in `Conv2DBackpropFilter` ### Impact An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`: ```python import tensorflow as tf input_tensor = tf.constant([386.078431372549, 386.07843139643234], shape=[1, 1, 1, 2], dtype=tf.float32) filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1], dtype=tf

CVE-2021-29578 [LOW] CWE-119 Heap buffer overflow in `FractionalAvgPoolGrad` Heap buffer overflow in `FractionalAvgPoolGrad` ### Impact The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64) out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_seq

CVE-2021-29577 [LOW] CWE-119 Heap buffer overflow in `AvgPool3DGrad` Heap buffer overflow in `AvgPool3DGrad` ### Impact The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32) grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" tf.raw_ops.AvgPool3DGrad( orig_inpu