PYSEC-2026-3159 Integer overflow in Tensorflow

Impact The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness: python import tensorflow as tf input = tf.constant1,1,dtype=tf.qint32 @tf.function def test: y = tf.rawops.Dequantize input=input, minrange=1.0, maxrange=10.0, mode='MINCOMBINED',...

PT-2026-59752

Impact The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer: cc const auto& init op sig it = meta graph def.signature def.findkSavedModelInitOpSignatureKey; if init op sig it != sig def map.end init op name = init op sig it-second.outputs...

PT-2026-59930

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...

PT-2026-59784

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

PT-2026-59721

Impact An attacker can trigger denial of service via assertion failure by altering a SavedModel on disk such that AttrDefs of some operation are duplicated. Patches We have patched the issue in GitHub commit c2b31ff2d3151acb230edc3f5b1832d2c713a9e0. The fix will be included in TensorFlow 2.8.0. W...

PT-2026-59970

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 ...

PT-2026-59953

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. int32 t. It also tried to access individual tensor bytes through tensor.data of size AllocatedBytes. This led to ASAN failures because t...

PT-2026-59926

Impact The implementation of tf.ragged.constant does not fully validate the input arguments. This results in a denial of service by consuming all available memory: python import tensorflow as tf tf.ragged.constantpylist=,ragged rank=8968073515812833920 Patches We have patched the issue in GitHub...

PT-2026-59889

Impact There is a potential for segfault / denial of service in TensorFlow by calling tf.compat.v1. ops which don't yet have support for quantized types added after migration to TF 2.x: python import numpy as np import tensorflow as tf tf.compat.v1.placeholder with...

PT-2026-59929

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

PT-2026-59892

Impact The tf.compat.v1.signal.rfft2d and tf.compat.v1.signal.rfft3d lack input validation and under certain condition can result in crashes due to CHECK-failures. Patches We have patched the issue in GitHub commit 0a8a781e597b18ead006d19b7d23d0a369e9ad73 merging GitHub PR 55274. The fix will be...

PT-2026-59840

Impact The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value: python import tensorflow as tf import numpy as np tf.raw ops.SparseTensorSliceDataset indices=, values=, dense shape=1,1 The 3 input arguments...

PT-2026-59848

Impact The implementation of Range suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations. Patches We have patched the issue in GitHub commit f0147751fd5d2ff23251149ebad9af9f03010732 merging 51733. The fix will be included in...

PT-2026-59873

Impact The implementation of SparseCountSparseOutput can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation: python import tensorflow as tf import numpy as np tf.raw ops.SparseCountSparseOutput indices=1,1, values=2, dense shape=2 31, 2 3...

PT-2026-59882

Impact The implementation of tf.raw ops.LSTMBlockCell 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 tf.raw ops.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...

PT-2026-59824

Impact The implementation of tf.raw ops.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 block shape = tf.constant-1879048192, shape=2, dtype=tf.int...

PT-2026-59768

Impact An attacker can craft a TFLite model that would cause an integer overflow in TfLiteIntArrayCreate: cc TfLiteIntArray TfLiteIntArrayCreateint size int alloc size = TfLiteIntArrayGetSizeInBytessize; // ... TfLiteIntArray ret = TfLiteIntArraymallocalloc size; // ... The...

PT-2026-59828

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: python import tensorflow as tf @tf.function def test: y = tf.raw ops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return y test The axis argumen...

PT-2026-59880

Impact The implementation of tf.raw ops.TensorSummaryV2 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 numpy as np import tensorflow as tf tf.raw ops.TensorSummaryV2 tag=np.array'test',...

PT-2026-59851

Impact The implementation of tf.raw ops.SparseTensorDenseAdd does not fully validate the input arguments: python import tensorflow as tf a indices = tf.constant0, shape=17, 2, dtype=tf.int64 a values = tf.constant, shape=0, dtype=tf.float32 a shape = tf.constant6, 12, shape=2, dtype=tf.int64 b =...