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Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

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...

5.5CVSS5.7AI score0.00395EPSS
SaveExploits1References14
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•5 views

PT-2026-59708

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,...

5.5CVSS5.8AI score0.00325EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•3 views

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',...

5.5CVSS5.7AI score0.00325EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•6 views

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 ...

7.5CVSS7.2AI score0.00488EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•8 views

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,...

5.5CVSS5.8AI score0.00341EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

PT-2026-59719

Impact A malicious user can cause a denial of service by altering a SavedModel such that TensorByteSize would trigger CHECK failures. cc int64 t TensorByteSizeconst TensorProto& t // num elements returns -1 if shape is not fully defined. int64 t num elems = TensorShapet.tensor shape.num elements;...

6.5CVSS6.5AI score0.00789EPSS
SaveExploits1References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

PT-2026-59846

Impact The implementation of AssignOp can result in copying unitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized to minimize number of allocations, but does not check that the right hand...

8.8CVSS7.6AI score0.00761EPSS
SaveExploits1References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

PT-2026-59949

Impact The implementation of tf.raw ops.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.raw ops.StagePeekindex=index,...

5.5CVSS5.8AI score0.00325EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

PT-2026-59956

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...

5.5CVSS5.7AI score0.00354EPSS
SaveExploits1References14
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•3 views

PT-2026-59958

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...

5.5CVSS5.8AI score0.00341EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•9 views

PT-2026-59732

Impact During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user: cc const auto num dims = Valueshape dim; std::vector dims; dims.reservenum dims; Patches We have patched the issue in GitHub commit 1361fb7e29449629e1df94d44e0427ebec8c83c7...

6.5CVSS6.7AI score0.00828EPSS
SaveExploits1References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•7 views

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...

8.8CVSS7.5AI score0.00818EPSS
SaveExploits1References11
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•6 views

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...

8.8CVSS7.4AI score0.00583EPSS
SaveExploits0References11
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•6 views

PT-2026-59765

Impact An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. Patches We have patched t...

8.8CVSS7.6AI score0.00544EPSS
SaveExploits0References9
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•8 views

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...

8.8CVSS7.9AI score0.00844EPSS
SaveExploits1References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•6 views

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...

8.8CVSS7.4AI score0.00794EPSS
SaveExploits1References11
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•4 views

PT-2026-59955

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.raw ops.UnsortedSegmentJoin inputs=tf.constant"this", shape=12,...

5.5CVSS5.8AI score0.00325EPSS
SaveExploits1References12
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•3 views

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...

6.5CVSS6.3AI score0.00765EPSS
SaveExploits1References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•7 views

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...

5.5CVSS5.9AI score0.0023EPSS
SaveExploits0References10
Positive Technologies
Positive Technologies
•added 2026/07/09 12:00 a.m.•5 views

PT-2026-59817

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...

5.5CVSS5.9AI score0.0023EPSS
SaveExploits0References10
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