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...

PYSEC-2026-3344 Integer overflow in `SpaceToBatchND`

Impact The implementation of tf.rawops.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 blockshape = tf.constant-1879048192, shape=2, dtype=tf.int64...

Missing validation results in undefined behavior in `QuantizedConv2D`

ImpactThe implementation of tf.rawops.QuantizedConv2D does not fully validate the input arguments:pythonimport tensorflow as tfinput = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8filter = tf.constant1, shape=1, 2, 3, 3, dtype=tf.quint8 bad argsmininput = tf.constant, shape=0,...

PYSEC-2026-3104 Segfault due to missing support for quantized types

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...

Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`

ImpactThe implementation of tf.rawops.SparseTensorToCSRSparseMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:pythonimport tensorflow as tfindices = tf.constant53, shape=3, dtype=tf.int64values =...

Missing validation causes denial of service via `UnsortedSegmentJoin`

ImpactThe implementation of tf.rawops.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:pythonimport tensorflow as tftf.rawops.UnsortedSegmentJoin inputs=tf.constant"this", shape=12,...

PYSEC-2026-3221 Missing validation causes denial of service via `LoadAndRemapMatrix`

Impact The implementation of tf.rawops.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 ckptpath = tf.constant...

PYSEC-2026-3267 Missing validation causes denial of service via `LSTMBlockCell`

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

PYSEC-2026-3195 Missing validation crashes `QuantizeAndDequantizeV4Grad`

Impact The implementation of tf.rawops.QuantizeAndDequantizeV4Grad 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.rawops.QuantizeAndDequantizeV4Grad gradients=tf.constant1,...

Missing validation causes denial of service via `StagePeek`

ImpactThe implementation of tf.rawops.StagePeek does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:pythonimport tensorflow as tfindex = tf.constant, shape=0, dtype=tf.int32tf.rawops.StagePeekindex=index,...

Missing validation causes denial of service via `DeleteSessionTensor`

ImpactThe implementation of tf.rawops.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:pythonimport tensorflow as tfhandle = tf.constant"", shape=0,...

PYSEC-2026-3196 Missing validation causes denial of service via `StagePeek`

Impact The implementation of tf.rawops.StagePeek 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 index = tf.constant, shape=0, dtype=tf.int32 tf.rawops.StagePeekindex=index,...

Memory leak in Tensorflow

ImpactIf a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize:ccStatus s = params.createkerneln-properties, &item-;kernel;if !s.ok item-kernel = nullptr; s = AttachDefs, n; return s; Here, we set item-kernel to nullptr but it is a simple...

Integer overflow in Tensorflow

ImpactThe implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements:ccint64t OpLevelCostEstimator::CalculateTensorSize const OpInfo::TensorProperties&...

PYSEC-2026-3138 Memory leak in Tensorflow

Impact If a graph node is invalid, TensorFlow can leak memory in the implementation of ImmutableExecutorState::Initialize: cc Status s = params.createkerneln-properties, &item-kernel; if !s.ok item-kernel = nullptr; s = AttachDefs, n; return s; Here, we set item-kernel to nullptr but it is a simp...

Division by zero in Tensorflow

Impact The estimator for the cost of some convolution operations can be made to execute a division by 0:pythonimport tensorflow as [email protected] test: y=tf.rawops.AvgPoolGrad originputshape=1,1,1,1, grad=1.0,1.0,1.0,2.0,2.0,2.0,3.0,3.0,3.0, ksize=1,1,1,1, strides=1,1,1,0, padding='VALID',...

PYSEC-2026-3090 Overflow and uncaught divide by zero in Tensorflow

Impact The implementation of UnravelIndex is vulnerable to a division by zero caused by an integer overflow bug: python import tensorflow as tf tf.rawops.UnravelIndexindices=-0x100000,dims=0x100000,0x100000 Patches We have patched the issue in GitHub commit 58b34c6c8250983948b5a781b426f6aa01fd47a...

Division by zero in TFLite

Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions.The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added befo...

PYSEC-2026-3094 Null pointer dereference in TensorFlow

Impact The implementation of QuantizedMaxPool has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. python import tensorflow as tf tf.rawops.QuantizedMaxPool input = tf.constant4, dtype=tf.quint8, mininput = , maxinput = 1, ksize = 1, 1, 1, 1,...

Assertion failure based denial of service in Tensorflow

Impact The implementation of Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail:pythonimport tensorflow as tftf.rawops.DenseBincount input=0, 1, 2, size=1, weights=3,2,1, binaryoutput=FalseThere are several conditions tha...