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-2026-59821

Impact When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow...

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 =...

PT-2026-59837

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-2026-59838

Impact The implementation of tf.raw ops.LoadAndRemapMatrix 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 ckpt path = tf.constant "/tmp/warm starting util...

PT-2026-59820

Impact The implementation of tf.raw ops.UnsortedSegmentJoin 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.strings.unsorted segment join inputs='123', segment ids=0, num...

PT-2026-59808

Impact A malicious user can cause a denial of service by altering a SavedModel such that any binary op would trigger CHECK failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the dtype no longer matches the dtype expected by the op. In that cas...

PT-2026-59814

Impact The implementation of tf.raw ops.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 tf.raw...

PT-2026-59774

Impact The TFG dialect of TensorFlow MLIR makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can...

PT-2026-59788

Impact The ScatterNd function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. Patches We have patched the issue in GitHub commit...

PT-2026-59775

Impact The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory: python import tensorflow as tf y = tf.raw ops.ThreadPoolHandlenum threads=0x60000000,display name='tf' This is because the num threads argument is only checked to not be...

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-2026-59754

Impact Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. Patches We have patched the issue in GitHub commi...

PT-2026-59716

Impact The implementation of SparseCountSparseOutput is vulnerable to a heap overflow: python import tensorflow as tf import numpy as np tf.raw ops.SparseCountSparseOutput indices=-1,-1, values=2, dense shape=1, 1, weights=1, binary output=True, minlength=-1, maxlength=-1, name=None Patches We ha...

PT-2026-59972

Impact The implementation of tf.raw ops.QuantizedConv2D does not fully validate the input arguments: python import tensorflow as tf input = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8 filter = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8 bad args min input = tf.constant, shape=0,...

PT-2026-59744

Impact There is a typo in TensorFlow's SpecializeType which results in heap OOB read/write: cc for int i = 0; i args size; j++ auto arg = t-mutable argsi; // ... Due to a typo, arg is initialized to the ith mutable argument in a loop where the loop index is j. Hence it is possible to assign to ar...

PT-2026-59771

Impact Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file fixing the first one would trigger the same dereference in the second place: First, during...

PT-2026-59739

Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batch dim and can result in a heap OOB read: python import tensorflow as tf @tf.function def test: y = tf.raw ops.ReverseSequence input = 'aaa','bbb', seq lengths = 1,1,1, seq dim = -10, batch di...

PT-2026-59751

Impact The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0: python import tensorflow as tf import numpy as np tf.raw ops.FractionalMaxPool value=tf.constantvalue=1, 4, 2, 3, dtype=tf.int64, pooling ratio=1.0, 1.44, 1.73, 1.0, pseudo random=False,...