PYSEC-2026-1004 Missing validation causes denial of service via `DeleteSessionTensor`

Impact The implementation of tf.rawops.DeleteSessionTensor does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf handle = tf.constant"", shape=0, dtype=tf.string...

Overflow in `ImageProjectiveTransformV2`

Impact When tf.rawops.ImageProjectiveTransformV2 is given a large output shape, it overflows. python import tensorflow as tf interpolation = "BILINEAR" fillmode = "REFLECT" images = tf.constant0.184634328, shape=2,5,8,3, dtype=tf.float32 transforms = tf.constant0.378575385, shape=2,8,...

TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

Impact FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack: python import tensorflow as tf overlapping = True originput = tf.constant.453409232,...

TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 features = tf.constant28, shape=4,2, dtype=tf.quint8 minfeatures...

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`

Impact If QuantizeDownAndShrinkRange is given nonscalar inputs for inputmin or inputmax, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 input = tf.constant1, shape=3, dtype=tf.qint32 inputmin = tf.constant,...

TensorFlow vulnerable to segfault in `QuantizedMatMul`

Impact If QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxb It gives a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf Toutput = tf.qint32 transposea = False transposeb = False Tactivation = tf.quint8 a = tf.constant7,...

TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

Impact If QuantizedBiasAdd is given mininput, maxinput, minbias, maxbias 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 outtype = tf.qint32 input = tf.constant85,170,255, shape=3, dtype=tf.quint8 bias =...

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 numbits = 8 narrowrange = False inputs = tf.constant0, shape=2,3, dtype=tf.float32 min = tf.constant0,...

TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`

Impact If QuantizedInstanceNorm is given xmin or xmax 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 outputrangegiven = False givenymin = 0 givenymax = 0 varianceepsilon = 1e-05 minseparation = 0.001 x =...

TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

Impact The implementation of Conv2DBackpropInput requires inputsizes 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" usecudnnongpu = True explicitpaddings =...

TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`

Impact The implementation of AvgPoolGrad does not fully validate the input originputshape. 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" dataformat = "NHWC"...

TensorFlow vulnerable to segfault in `LowerBound` and `UpperBound`

Impact If LowerBound or UpperBound is given an emptysortedinputs 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 outtype = tf.int32 sortedinputs = tf.constant, shape=10,0, dtype=tf.float32...

TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`

Impact The implementation of AvgPool3DGradOp does not fully validate the input originputshape. 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 ...

TensorFlow vulnerable to segfault in `Requantize`

Impact If Requantize is given inputmin, inputmax, requestedoutputmin, requestedoutputmax 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 outtype = tf.quint8 input = tf.constant1, shape=3, dtype=tf.qint32...

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 ...

`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 =...

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 =...

Integer overflow in `SpaceToBatchND`

Impact The implementation of tf.rawops.SpaceToBatchND in all backends such as XLA and handwritten kernels is vulnerable to an integer overflow: python import tensorflow as tf input = tf.constant-3.5e+35, shape=10,19,22, dtype=tf.float32 blockshape = tf.constant-1879048192, shape=2, dtype=tf.int64...