TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

ImpactFractionalMaxPoolGrad 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:pythonimport tensorflow as tfoverlapping = Trueoriginput = tf.constant.453409232,...

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

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.quint8features = tf.constant28, shape=4,2, dtype=tf.quint8minfeatures =...

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.quint8input = tf.constant1, shape=3, dtype=tf.qint32inputmin = tf.constant, shape=0,...

TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

ImpactFractionalMaxPoolGrad 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:pythonimport tensorflow as tfoverlapping = Trueoriginput = tf.constant.453409232,...

TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`

ImpactIf 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.pythonimport tensorflow as tfoutputrangegiven = Falsegivenymin = 0givenymax = 0varianceepsilon = 1e-05minseparation = 0.001x =...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`

ImpactIf 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.pythonimport tensorflow as tfnumbits = 8narrowrange = Falseinputs = tf.constant0, shape=2,3, dtype=tf.float32min = tf.constant0,...

TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

ImpactThe 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:pythonimport tensorflow as tfstrides = 1, 1, 1, 1padding = "SAME"usecudnnongpu = Trueexplicitpaddings = dataformat =...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`

ImpactIf 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.pythonimport tensorflow as tfnumbits = 8narrowrange = Falseinputs = tf.constant0, shape=2,3, dtype=tf.float32min = tf.constant0,...

TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

ImpactThe 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:pythonimport tensorflow as tfstrides = 1, 1, 1, 1padding = "SAME"usecudnnongpu = Trueexplicitpaddings = dataformat =...

TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`

ImpactIf 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.pythonimport tensorflow as tfoutputrangegiven = Falsegivenymin = 0givenymax = 0varianceepsilon = 1e-05minseparation = 0.001x =...

TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.qint32input = tf.constant85,170,255, shape=3, dtype=tf.quint8bias =...

TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.qint32input = tf.constant85,170,255, shape=3, dtype=tf.quint8bias =...

TensorFlow vulnerable to segfault in `QuantizedAvgPool`

ImpactIf QuantizedAvgPool is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfksize = 1, 2, 2, 1strides = 1, 2, 2, 1padding = "SAME"input = tf.constant1, shape=1,4,4,2,...

TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`

ImpactThe 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:pythonimport tensorflow as tfksize = 1, 2, 2, 1strides = 1, 2, 2, 1padding = "VALID"dataformat = "NHWC"originputshape =...

TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`

ImpactThe 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:pythonimport tensorflow as tfksize = 1, 2, 2, 1strides = 1, 2, 2, 1padding = "VALID"dataformat = "NHWC"originputshape =...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

ImpactWhen tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=1,1, dtype=tf.float32,...

TensorFlow vulnerable to `CHECK` fail in `TensorListScatter` and `TensorListScatterV2`

ImpactWhen TensorListScatter and TensorListScatterV2 receive an elementshape of a rank greater than one, they give a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=2, 2, 2, dtype=tf.float16, maxval=Nonearg1=tf.random.uniformshape=2,...

TensorFlow vulnerable to segfault in `QuantizedAdd`

ImpactIf QuantizedAdd is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfToutput = tf.qint32x = tf.constant140, shape=1, dtype=tf.quint8y = tf.constant26, shape=10, dtype=tf.quint8mi...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

ImpactWhen tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=1,1, dtype=tf.float32,...

TensorFlow vulnerable to segfault in `QuantizedAvgPool`

ImpactIf QuantizedAvgPool is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfksize = 1, 2, 2, 1strides = 1, 2, 2, 1padding = "SAME"input = tf.constant1, shape=1,4,4,2,...

TensorFlow vulnerable to segfault in `QuantizedAdd`

ImpactIf QuantizedAdd is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfToutput = tf.qint32x = tf.constant140, shape=1, dtype=tf.quint8y = tf.constant26, shape=10, dtype=tf.quint8mi...

TensorFlow vulnerable to segfault in `BlockLSTMGradV2`

ImpactThe implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3This results in a a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfusepeephole =...

TensorFlow vulnerable to segfault in `BlockLSTMGradV2`

ImpactThe implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3This results in a a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfusepeephole =...

TensorFlow vulnerable to `CHECK` fail in `TensorListScatter` and `TensorListScatterV2`

ImpactWhen TensorListScatter and TensorListScatterV2 receive an elementshape of a rank greater than one, they give a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=2, 2, 2, dtype=tf.float16, maxval=Nonearg1=tf.random.uniformshape=2,...

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

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.int32sortedinputs = tf.constant, shape=10,0, dtype=tf.float32values =...

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

ImpactIf 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.pythonimport tensorflow as tfouttype = tf.int32sortedinputs = tf.constant, shape=10,0, dtype=tf.float32values =...

TensorFlow vulnerable to `CHECK` fail in `TensorListFromTensor`

ImpactWhen TensorListFromTensor receives an elementshape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=6, 6, 2, dtype=tf.bfloat16, maxval=Nonearg1=tf.random.uniformshape=6, 9, 1, 3,...

TensorFlow vulnerable to `CHECK` fail in `TensorListFromTensor`

ImpactWhen TensorListFromTensor receives an elementshape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=6, 6, 2, dtype=tf.bfloat16, maxval=Nonearg1=tf.random.uniformshape=6, 9, 1, 3,...

TensorFlow vulnerable to `CHECK` fail in `SetSize`

ImpactWhen SetSize receives an input setshape that is not a 1D tensor, it gives a CHECK fails that can be used to trigger a denial of service attack.pythonimport tensorflow as tfarg0=1arg1=1,1arg2=1arg3=Truearg4=''tf.rawops.SetSizesetindices=arg0, setvalues=arg1, setshape=arg2,...

TensorFlow vulnerable to `CHECK` fail in `SetSize`

ImpactWhen SetSize receives an input setshape that is not a 1D tensor, it gives a CHECK fails that can be used to trigger a denial of service attack.pythonimport tensorflow as tfarg0=1arg1=1,1arg2=1arg3=Truearg4=''tf.rawops.SetSizesetindices=arg0, setvalues=arg1, setshape=arg2,...

TensorFlow vulnerable to `CHECK` failures in `UnbatchGradOp`

ImpactThe UnbatchGradOp function takes an argument id that is assumed to be a scalar. A nonscalar id can trigger a CHECK failure and crash the program.pythonimport numpy as npimport tensorflow as tf id is not scalartf.rawops.UnbatchGradoriginalinput= tf.constant1,batchindex=tf.constant0,0,0 , ,...

TensorFlow vulnerable to Int overflow in `RaggedRangeOp`

ImpactThe RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the...

TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`

ImpactThe implementation of FractionalAvgPoolGrad does not fully validate the input originputtensorshape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack.pythonimport tensorflow as tfoverlapping = Trueoriginputtensorshape =...

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

ImpactIn core/kernels/listkernels.cc's TensorListReserve, numelements is assumed to be a tensor of size 1. When a numelements of more than 1 element is provided, then tf.rawops.TensorListReserve fails the CHECKEQ in CheckIsAlignedAndSingleElement.pythonimport tensorflow as...

TensorFlow vulnerable to `CHECK` failures in `UnbatchGradOp`

ImpactThe UnbatchGradOp function takes an argument id that is assumed to be a scalar. A nonscalar id can trigger a CHECK failure and crash the program.pythonimport numpy as npimport tensorflow as tf id is not scalartf.rawops.UnbatchGradoriginalinput= tf.constant1,batchindex=tf.constant0,0,0 , ,...

TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`

ImpactThe implementation of FractionalAvgPoolGrad does not fully validate the input originputtensorshape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack.pythonimport tensorflow as tfoverlapping = Trueoriginputtensorshape =...

TensorFlow vulnerable to Int overflow in `RaggedRangeOp`

ImpactThe RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the...

TensorFlow vulnerable to `CHECK` fail in `CollectiveGather`

ImpactWhen CollectiveGather receives an scalar input input, it gives a CHECK fails that can be used to trigger a denial of service attack.pythonimport tensorflow as tfarg0=1arg1=1arg2=1arg3=1arg4=3, 3,3arg5='auto'arg6=0arg7=''tf.rawops.CollectiveGatherinput=arg0, groupsize=arg1, groupkey=arg2,...

TensorFlow vulnerable to `CHECK` fail in `CollectiveGather`

ImpactWhen CollectiveGather receives an scalar input input, it gives a CHECK fails that can be used to trigger a denial of service attack.pythonimport tensorflow as tfarg0=1arg1=1arg2=1arg3=1arg4=3, 3,3arg5='auto'arg6=0arg7=''tf.rawops.CollectiveGatherinput=arg0, groupsize=arg1, groupkey=arg2,...

TensorFlow vulnerable to floating point exception in `Conv2D`

ImpactIf 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.pythonimport tensorflow as tfimport numpy as npwith tf.device"CPU": also can be...

TensorFlow vulnerable to `CHECK` fail in `DrawBoundingBoxes`

ImpactWhen DrawBoundingBoxes receives an input boxes that is not of dtype float, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfimport numpy as nparg0=tf.constantvalue=np.random.randomsize=1, 3, 2, 3, shape=1, 3, 2, 3,...

TensorFlow vulnerable to null dereference on MLIR on empty function attributes

ImpactEig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfimport numpy as np arg0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2, dtype=tf.float32arg1=tf.complex128arg2=Truearg3=''tf.rawops.Eiginput=arg...

TensorFlow vulnerable to `CHECK` fail in `DrawBoundingBoxes`

ImpactWhen DrawBoundingBoxes receives an input boxes that is not of dtype float, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfimport numpy as nparg0=tf.constantvalue=np.random.randomsize=1, 3, 2, 3, shape=1, 3, 2, 3,...

TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`

ImpactThe 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:pythonimport tensorflow as tfksize = 1, 1, 1, 1, 1strides = 1, 1, 1, 1, 1padding =...

TensorFlow vulnerable to null dereference on MLIR on empty function attributes

ImpactEig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfimport numpy as np arg0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2, dtype=tf.float32arg1=tf.complex128arg2=Truearg3=''tf.rawops.Eiginput=arg...

TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`

ImpactThe 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:pythonimport tensorflow as tfksize = 1, 1, 1, 1, 1strides = 1, 1, 1, 1, 1padding =...

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

ImpactIn core/kernels/listkernels.cc's TensorListReserve, numelements is assumed to be a tensor of size 1. When a numelements of more than 1 element is provided, then tf.rawops.TensorListReserve fails the CHECKEQ in CheckIsAlignedAndSingleElement.pythonimport tensorflow as...

TensorFlow vulnerable to floating point exception in `Conv2D`

ImpactIf 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.pythonimport tensorflow as tfimport numpy as npwith tf.device"CPU": also can be...

TensorFlow vulnerable to `CHECK` fail in `Unbatch`

ImpactWhen Unbatch receives a nonscalar input id, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfimport numpy as nparg0=tf.constantvalue=np.random.randomsize=3, 3, 1, dtype=tf.float64arg1=tf.constantvalue=np.random.randint0,100,size=3, 3, 1,...

TensorFlow vulnerable to null dereference on MLIR on empty function attributes

ImpactWhen mlir::tfg::ConvertGenericFunctionToFunctionDef is given empty function attributes, it gives a null dereference.cpp// Import the function attributes with a tf. prefix to match the current// infrastructure expectations.for const auto& namedAttr : func.attr const std::string& name = "tf."...