PT-2026-59872

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 paddin...

PT-2026-59779

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg 1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=No...

PT-2026-59974

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 =...

PT-2026-59980

Impact If the stride and window size are not positive for tf.raw ops.AvgPoolGrad, it can give an FPE. python import tensorflow as tf import numpy as np @tf.functionjit compile=True def test: y = tf.raw ops.AvgPoolGradorig input shape=1,0,0,0, grad=0.39117979, ksize=1,0,0,0, strides=1,0,0,0,...

PT-2026-59871

Impact If Requantize is given input min, input max, requested output min, requested output max 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 out type = tf.quint8 input = tf.constant1, shape=3,...

PT-2026-59877

Impact If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a nullptr, which is not caught. An example can be seen in tf.compat.v1.extract volume patches by passing in quantized tensors as input ksizes. python import numpy as np import...

PT-2026-59992

Impact When SetSize receives an input set shape that is not a 1D tensor, it gives a CHECK fails that can be used to trigger a denial of service attack. python import tensorflow as tf arg 0=1 arg 1=1,1 arg 2=1 arg 3=True arg 4='' tf.raw ops.SetSizeset indices=arg 0, set values=arg 1, set shape=arg...

PT-2026-59705

Impact Inputs dense features or example state data not of rank 2 will trigger a CHECK fail in SdcaOptimizer. python import tensorflow as tf tf.raw ops.SdcaOptimizer sparse example indices=4 tf.random.uniform5,5,5,3, dtype=tf.dtypes.int64, maxval=100, sparse feature indices=4...

PT-2026-59968

Impact When tf.random.gamma receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg 1=tf.random.uniformshape=4, 4, dtype=tf.float64, maxval=None arg...

PT-2026-59936

Impact Eig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf import numpy as np arg 0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2, dtype=tf.float32 arg 1=tf.complex128 arg 2=True arg 3='' tf.raw...

PT-2026-59914

Impact If the parameter indices for DynamicStitch does not match the shape of the parameter data, it can trigger an stack OOB read. python import tensorflow as tf func = tf.raw ops.DynamicStitch para='indices': 0xdeadbeef, 405, 519, 758, 1015, 'data': 110.27793884277344, 120.29475402832031,...

PT-2026-59860

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

PT-2026-59993

Impact If Requantize is given input min, input max, requested output min, requested output max 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 out type = tf.quint8 input = tf.constant1, shape=3,...

PT-2026-59870

Impact When SetSize receives an input set shape that is not a 1D tensor, it gives a CHECK fails that can be used to trigger a denial of service attack. python import tensorflow as tf arg 0=1 arg 1=1,1 arg 2=1 arg 3=True arg 4='' tf.raw ops.SetSizeset indices=arg 0, set values=arg 1, set shape=arg...

PT-2026-59998

Impact If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a nullptr, which is not caught. An example can be seen in tf.compat.v1.extract volume patches by passing in quantized tensors as input ksizes. python import numpy as np import...

PT-2026-59879

Impact Inputs dense features or example state data not of rank 2 will trigger a CHECK fail in SdcaOptimizer. python import tensorflow as tf tf.raw ops.SdcaOptimizer sparse example indices=4 tf.random.uniform5,5,5,3, dtype=tf.dtypes.int64, maxval=100, sparse feature indices=4...

PT-2026-59912

Impact When the BaseCandidateSamplerOp function receives a value in true classes larger than range max, a heap oob vuln occurs. python tf.raw ops.ThreadUnsafeUnigramCandidateSampler true classes=0x100000,1, num true = 2, num sampled = 2, unique = False, range max = 2, seed = 2, seed2 = 2 Patches ...

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

ImpactThe 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.pythonimport tensorflow as tftf.rawops.SobolSampledim=tf.constant1,0, numresults=tf.constant1, skip=tf.constant1 PatchesWe...

PYSEC-2026-3280 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...

PYSEC-2026-3167 Type confusion leading to `CHECK`-failure based denial of service in TensorFlow

Impact The macros that TensorFlow uses for writing assertions e.g., CHECKLT, CHECKGT, etc. have an incorrect logic when comparing sizet and int values. Due to type conversion rules, several of the macros would trigger incorrectly. Patches We have patched the issue in GitHub commit...