Impact arrayops.upperbound causes a segfault when not given a rank 2 tensor.

Patches We have patched the issue in GitHub commit 915884fdf5df34aaedd00fc6ace33a2cfdefa586.

The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow 2.12.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by dmc1778

TensorFlow is an open source platform for machine learning. An input token that is not a UTF-8 bytestring will trigger a CHECK fail in tf.rawops.PyFunc. We have patched the issue in GitHub commit 9f03a9d3bafe902c1e6beb105b2f24172f238645. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When tf.rawops.FusedResizeAndPadConv2D is given a large tensor shape, it overflows. We have patched the issue in GitHub commit d66e1d568275e6a2947de97dca7a102a211e01ce. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When tf.rawops.ImageProjectiveTransformV2 is given a large output shape, it overflows. We have patched the issue in GitHub commit 8faa6ea692985dbe6ce10e1a3168e0bd60a723ba. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When the BaseCandidateSamplerOp function receives a value in trueclasses larger than rangemax, a heap oob read occurs. We have patched the issue in GitHub commit b389f5c944cadfdfe599b3f1e4026e036f30d2d4. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. Inputs densefeatures or examplestatedata not of rank 2 will trigger a CHECK fail in SdcaOptimizer. We have patched the issue in GitHub commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When tf.rawops.ResizeNearestNeighborGrad is given a large size input, it overflows. We have patched the issue in GitHub commit 00c821af032ba9e5f5fa3fe14690c8d28a657624. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. An input encoded that is not a valid CompositeTensorVariant tensor will trigger a segfault in tf.rawops.CompositeTensorVariantToComponents. We have patched the issue in GitHub commits bf594d08d377dc6a3354d9fdb494b32d45f91971 and 660ce5a89eb6766834bdc303d2ab3902aef99d3d. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If BCast::ToShape is given input larger than an int32, it will crash, despite being supposed to handle up to an int64. An example can be seen in tf.experimental.numpy.outer by passing in large input to the input b. We have patched the issue in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. An input sparsematrix that is not a matrix with a shape with rank 0 will trigger a CHECK fail in tf.rawops.SparseMatrixNNZ. We have patched the issue in GitHub commit f856d02e5322821aad155dad9b3acab1e9f5d693. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If SparseFillEmptyRowsGrad is given empty inputs, TensorFlow will crash. We have patched the issue in GitHub commit af4a6a3c8b95022c351edae94560acc61253a1b8. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When running on GPU, tf.image.generateboundingboxproposals receives a scores input that must be of rank 4 but is not checked. We have patched the issue in GitHub commit cf35502463a88ca7185a99daa7031df60b3c1c98. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If tf.rawops.TensorListConcat is given elementshape=[], it results segmentation fault which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit fc33f3dc4c14051a83eec6535b608abe1d355fde. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When ops that have specified input sizes receive a differing number of inputs, the executor will crash. We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. If FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash. We have patched the issue in GitHub commit d71090c3e5ca325bdf4b02eb236cfb3ee823e927. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. The reference kernel of the CONV3DTRANSPOSE TensorFlow Lite operator wrongly increments the dataptr when adding the bias to the result. Instead of dataptr += numchannels; it should be dataptr += outputnumchannels; as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if numchannels > outputnumchannels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. tf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.

TensorFlow is an open source platform for machine learning. If ThreadUnsafeUnigramCandidateSampler is given input filterbankchannelcount greater than the allowed max size, TensorFlow will crash. We have patched the issue in GitHub commit 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

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.extractvolumepatches by passing in quantized tensors as input ksizes. python import numpy as np import tensorflow as tf

ainput = np.array([1, -1], dtype= np.int32) aksizes = astrides = tf.constant(dtype=tf.dtypes.qint16, value=[[1, 4], [5, 2]])

tf.compat.v1.extractvolumepatches(input=ainput,ksizes=aksizes,strides=astrides,padding='VALID')

Patches We have patched the issue in GitHub commit e9e95553e5411834d215e6770c81a83a3d0866ce.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by Pattarakrit Rattankul.

TensorFlow is an open source platform for machine learning. If tf.rawops.TensorListResize is given a nonscalar value for input size, it results CHECK fail which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 888e34b49009a4e734c27ab0c43b0b5102682c56. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. When printing a tensor, we get it's data as a const char array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from char to bool are undefined if the char is not 0 or 1, so sanitizers/fuzzers will crash. The issue has been patched in GitHub commit 1be74370327. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.10.1, TensorFlow 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. The security vulnerability results in FractionalMax(AVG)Pool with illegal poolingratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution. We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1.

TensorFlow is an open source platform for machine learning. If MirrorPadGrad is given outsize input paddings, TensorFlow will give a heap OOB error. We have patched the issue in GitHub commit 717ca98d8c3bba348ff62281fdf38dcb5ea1ec92. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Impact When running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault.

ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types.

Patches We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Impact TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format.

python from tensorflow.keras import models

payload = ''' !!python/object/new:type args: ['z', !!python/tuple [], {'extend': !!python/name:exec }] listitems: "import('os').system('cat /etc/passwd')" ''' models.modelfromyaml(payload) The implementation uses yaml.unsafeload which can perform arbitrary code execution on the input.

Patches Given that YAML format support requires a significant amount of work, we have removed it for now.

We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by Arjun Shibu.

Impact Under certain conditions, Go code can trigger a segfault in string deallocation.

For string tensors, C.TFTStringDealloc is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions).

To fix this, the call to set the finalizer function is deferred until NewTensor returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written.

Patches We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22 (merging #50508).

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported externally via a fixing PR.

Impact An attacker can cause denial of service in applications serving models using tf.rawops.NonMaxSuppressionV5 by triggering a division by 0:

python import tensorflow as tf

tf.rawops.NonMaxSuppressionV5( boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]], scores=[1.0,2.0,3.0], maxoutputsize=-1, iouthreshold=0.5, scorethreshold=0.5, softnmssigma=1.0, padtomaxoutputsize=True) The implementation uses a user controlled argument to resize a std::vector:

cc const int outputsize = maxoutputsize.scalar<int>()(); // ... std::vector<int> selected; // ... if (padtomaxoutputsize) { selected.resize(outputsize, 0); // ... } However, as std::vector::resize takes the size argument as a sizet and outputsize is an int, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.

A similar issue occurs in CombinedNonMaxSuppression:

python import tensorflow as tf

tf.rawops.NonMaxSuppressionV5( boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]], scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]], maxoutputsizeperclass=-1, maxtotalsize=10, iouthreshold=scorethreshold=0.5, padperclass=True, clipboxes=True) Patches We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Impact An attacker can trigger a denial of service via a CHECK-fail in tf.rawops.MapStage:

python import tensorflow as tf tf.rawops.MapStage( key=tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), indices=tf.constant((0), dtype=tf.int32), values=[tf.constant((0), dtype=tf.int32)], dtypes=[tf.int32, tf.int64], capacity=0, memorylimit=0, container='', sharedname='') The implementation does not check that the key input is a valid non-empty tensor. Patches We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Impact Due to incomplete validation in tf.rawops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:

python import tensorflow as tf

tf.rawops.QuantizeV2( input=[1,2,3], minrange=[1,2], maxrange=[], T=tf.qint32, mode='SCALED', roundmode='HALFAWAYFROMZERO', narrowrange=False, axis=1, ensureminimumrange=3)

The implementation has some validation but does not check that minrange and maxrange both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of minrange and maxrange inputs match the axis dimension of the input tensor. Patches We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.