Intel Optimization For Tensorflow vulnerabilities

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-2022-35984 [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=

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-2022-35959 [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-2023-27579 [HIGH] CWE-697 TensorFlow has Floating Point Exception in TFLite in conv kernel TensorFlow has Floating Point Exception in TFLite in conv kernel ### Impact Constructing a tflite model with a paramater `filter_input_channel` of less than 1 gives a FPE. ### Patches We have patched the issue in GitHub commit [34f8368c535253f5c9cb3a303297743b62442aaa](https://github.com/tensorflow/tensorflow/commit/34f8368c535253f5c9cb3a303297743b62442aaa). The fix will be included in TensorFlow 2

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-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-2023-25672 [HIGH] CWE-476 TensorFlow has Null Pointer Error in LookupTableImportV2 TensorFlow has Null Pointer Error in LookupTableImportV2 ### Impact The function `tf.raw_ops.LookupTableImportV2` cannot handle scalars in the `values` parameter and gives an NPE. ```python import tensorflow as tf v = tf.Variable(1) @tf.function(jit_compile=True) def test(): func = tf.raw_ops.LookupTableImportV2 para={'table_handle': v.handle,'keys': [62.98910140991211, 94.36528015136719], 'values': -919}

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