Tensorflow vulnerable to Out-of-Bounds Read

ImpactWhen the BaseCandidateSamplerOp function receives a value in trueclasses larger than rangemax, a heap oob vuln occurs.pythontf.rawops.ThreadUnsafeUnigramCandidateSampler trueclasses=0x100000,1, numtrue = 2, numsampled = 2, unique = False, rangemax = 2, seed = 2, seed2 = 2 PatchesWe have...

PYSEC-2026-3298 Tensorflow vulnerable to Out-of-Bounds Read

Impact When the BaseCandidateSamplerOp function receives a value in trueclasses larger than rangemax, a heap oob vuln occurs. python tf.rawops.ThreadUnsafeUnigramCandidateSampler trueclasses=0x100000,1, numtrue = 2, numsampled = 2, unique = False, rangemax = 2, seed = 2, seed2 = 2 Patches We have...

PYSEC-2026-3261 FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess

Impact An input poolingratio that is smaller than 1 will trigger a heap OOB in tf.rawops.FractionalMaxPool and tf.rawops.FractionalAvgPool. Patches We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cher...

PYSEC-2026-3383 FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess

Impact An input poolingratio that is smaller than 1 will trigger a heap OOB in tf.rawops.FractionalMaxPool and tf.rawops.FractionalAvgPool. Patches We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cher...

FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess

ImpactAn input poolingratio that is smaller than 1 will trigger a heap OOB in tf.rawops.FractionalMaxPool and tf.rawops.FractionalAvgPool. PatchesWe have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48.The fix will be included in TensorFlow 2.11.0. We will also cherry...

`tf.raw_ops.Mfcc` crashes

ImpactIf ThreadUnsafeUnigramCandidateSampler is given input filterbankchannelcount greater than the allowed max size, TensorFlow will crash.pythonimport tensorflow as tftf.rawops.Mfcc spectrogram = 1.38, 6.32, 5.75, 9.51, samplerate = 2, upperfrequencylimit = 5.0, lowerfrequencylimit = 1.0,...

PYSEC-2026-3313 `FractionalMaxPoolGrad` Heap out of bounds read

Impact If FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash. python import tensorflow as tf tf.rawops.FractionMaxPoolGrad originput = 1, 1, 1, 1, 1, origoutput = 1, 1, 1, outbackprop = 3, 3, 6, rowpoolingsequence = -0x4000000, 1, 1,...

PYSEC-2026-3165 `FractionalMaxPoolGrad` Heap out of bounds read

Impact If FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash. python import tensorflow as tf tf.rawops.FractionMaxPoolGrad originput = 1, 1, 1, 1, 1, origoutput = 1, 1, 1, outbackprop = 3, 3, 6, rowpoolingsequence = -0x4000000, 1, 1,...

PYSEC-2026-3375 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 `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,...

PYSEC-2026-3372 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 `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,...

PYSEC-2026-3245 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 `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 `QuantizedMatMul`

ImpactIf QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxbIt gives a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfToutput = tf.qint32transposea = Falsetransposeb = FalseTactivation = tf.quint8a = tf.constant7, shape=3,4,...

PYSEC-2026-3250 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 `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 `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 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 =...

PYSEC-2026-3338 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

Impact When 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. python import tensorflow as tf arg0=tf.random.uniformshape=1,1, dtype=tf.float32, maxval=None...