GHSA-2R2F-G8MW-9GVR Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow

Impact The implementation of tf.rawops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service: python import tensorflow as tf hypothesisindices = tf.constant-1250999896764, shape=3, 3, dtype=tf.int64 hypothesisvalues =...

GHSA-5WPJ-C6F7-24X8 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...

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...

GHSA-RC9W-5C64-9VQQ Missing validation results in undefined behavior in `SparseTensorDenseAdd

Impact The implementation of tf.rawops.SparseTensorDenseAdd does not fully validate the input arguments: python import tensorflow as tf aindices = tf.constant0, shape=17, 2, dtype=tf.int64 avalues = tf.constant, shape=0, dtype=tf.float32 ashape = tf.constant6, 12, shape=2, dtype=tf.int64 b =...

Missing validation results in undefined behavior in `SparseTensorDenseAdd

Impact The implementation of tf.rawops.SparseTensorDenseAdd does not fully validate the input arguments: python import tensorflow as tf aindices = tf.constant0, shape=17, 2, dtype=tf.int64 avalues = tf.constant, shape=0, dtype=tf.float32 ashape = tf.constant6, 12, shape=2, dtype=tf.int64 b =...

GHSA-54CH-GJQ5-4976 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...

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...

GHSA-HX9Q-2MX4-M4PG Missing validation causes denial of service via `Conv3DBackpropFilterV2`

Impact The 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: python import tensorflow as tf tf.strings.unsortedsegmentjoin inputs='123', segmentids=0, numsegments=-1...

Missing validation causes denial of service via `Conv3DBackpropFilterV2`

Impact The 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: python import tensorflow as tf tf.strings.unsortedsegmentjoin inputs='123', segmentids=0, numsegments=-1...

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...

GHSA-JJM6-4VF7-CJH4 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...

Denial of service in `tf.ragged.constant` due to lack of validation

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=,raggedrank=8968073515812833920 Patches We have patched the issue in GitHub...

GHSA-CWPM-F78V-7M5C Denial of service in `tf.ragged.constant` due to lack of validation

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=,raggedrank=8968073515812833920 Patches We have patched the issue in GitHub...

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact The implementation of tf.rawops.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 mininput = tf.constant, shape=0,...

GHSA-PQHM-4WVF-2JG8 Missing validation results in undefined behavior in `QuantizedConv2D`

Impact The implementation of tf.rawops.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 mininput = tf.constant, shape=0,...

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,...

GHSA-2VV3-56QG-G2CF 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,...

GHSA-P9RC-RMR5-529J 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...

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...

Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`

Impact The 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: python import tensorflow as tf indices = tf.constant53, shape=3, dtype=tf.int64 values =...