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Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•35 views

PT-2026-59891

Impact When SparseSparseMaximum is given invalid sparse tensors as inputs, it can give an NPE. python import tensorflow as tf tf.raw ops.SparseSparseMaximum a indices=1, a values = 0.1 , a shape = 2, b indices=, b values =2 , b shape = 2, Patches We have patched the issue in GitHub commit...

7.5CVSS6.5AI score0.00443EPSS
SaveExploits1References7
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•31 views

PT-2026-59867

Impact DenseBincount assumes its input tensor weights to either have the same shape as its input tensor input or to be length-0. A different weights shape will trigger a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf binary output = True input =...

7.5CVSS7AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•24 views

PT-2026-59963

Impact If Save or SaveSlices is run over tensors of an unsupported dtype, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf filename = tf.constant"" tensor names = tf.constant"" Save data = tf.casttf.random.uniformshape=1,...

7.5CVSS7.1AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•39 views

PT-2026-59839

Impact 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. Patches We have patched the issu...

7.5CVSS7.4AI score0.00444EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•30 views

PT-2026-59769

Impact If FakeQuantWithMinMaxVarsPerChannel is given min or max tensors of a rank other than one, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf num bits = 8 narrow range = False inputs = tf.constant0, shape=4, dtype=tf.float32 mi...

7.5CVSS7AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•53 views

PT-2026-59899

Impact If tf.raw ops.TensorListResize is given a nonscalar value for input size, it results CHECK fail which can be used to trigger a denial of service attack. python import numpy as np import tensorflow as tf a = data structures.tf tensor list newelements = tf.constantvalue=3, 4, 5 b = np.zeros0...

7.5CVSS7.1AI score0.00493EPSS
SaveExploits1References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•31 views

PT-2026-59879

Impact Inputs dense features or example state data not of rank 2 will trigger a CHECK fail in SdcaOptimizer. python import tensorflow as tf tf.raw ops.SdcaOptimizer sparse example indices=4 tf.random.uniform5,5,5,3, dtype=tf.dtypes.int64, maxval=100, sparse feature indices=4...

7.5CVSS7.1AI score0.00494EPSS
SaveExploits1References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•27 views

PT-2026-59911

Impact tf.keras.losses.poisson receives a y pred and y true 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. python import numpy as np import tensorflow as tf true value =...

7.5CVSS7.3AI score0.00493EPSS
SaveExploits1References9
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•25 views

PT-2026-59904

Impact When tf.raw ops.FusedResizeAndPadConv2D is given a large tensor shape, it overflows. python import tensorflow as tf mode = "REFLECT" strides = 1, 1, 1, 1 padding = "SAME" resize align corners = False input = tf.constant147, shape=3,3,1,1, dtype=tf.float16 size =...

7.5CVSS6.9AI score0.00472EPSS
SaveExploits1References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•21 views

PT-2026-59852

Impact If the stride and window size are not positive for tf.raw ops.AvgPoolGrad, it can give an FPE. python import tensorflow as tf import numpy as np @tf.functionjit compile=True def test: y = tf.raw ops.AvgPoolGradorig input shape=1,0,0,0, grad=0.39117979, ksize=1,0,0,0, strides=1,0,0,0,...

7.5CVSS6.4AI score0.00394EPSS
SaveExploits0References7
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•33 views

PT-2026-59981

Impact When RangeSize receives values that do not fit into an int64 t, it crashes. cpp auto size = std::is integral::value ? Eigen::numext::abslimit - start + Eigen::numext::absdelta - T1 / Eigen::numext::absdelta : Eigen::numext::ceil Eigen::numext::abslimit - start / delta; // This check does n...

7.5CVSS7.3AI score0.00701EPSS
SaveExploits0References9
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•24 views

PT-2026-59772

Impact When TensorListFromTensor receives an element shape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=6, 6, 2, dtype=tf.bfloat16, maxval=None arg 1=tf.random.uniformshape=6, 9, 1, 3,...

7.5CVSS7.1AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•39 views

PT-2026-59912

Impact When the BaseCandidateSamplerOp function receives a value in true classes larger than range max, a heap oob vuln occurs. python tf.raw ops.ThreadUnsafeUnigramCandidateSampler true classes=0x100000,1, num true = 2, num sampled = 2, unique = False, range max = 2, seed = 2, seed2 = 2 Patches ...

9.1CVSS7.9AI score0.00427EPSS
SaveExploits1References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•26 views

PT-2026-59866

Impact tf.raw ops.DynamicStitch specifies input sizes when it is registered. cpp REGISTER OP"DynamicStitch" .Input"indices: N int32" .Input"data: N T" .Output"merged: T" .Attr"N : int = 1" .Attr"T : type" .SetShapeFnDynamicStitchShapeFunction; When it receives a differing number of inputs, such a...

7.5CVSS7.2AI score0.00384EPSS
SaveExploits1References9
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•22 views

PT-2026-59844

Impact The implementation of Conv2DBackpropInput requires input sizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger a denial of service attack: python import tensorflow as tf strides = 1, 1, 1, 1 padding = "SAME" use cudnn on gpu = True explicit paddings =...

7.5CVSS7AI score0.00462EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•40 views

PT-2026-59745

Impact When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. python import tensorflow as tf class QuantConv2DTransposedtf.keras.layers.Layer: def buildself, input shape: self.kernel = self.add weight"kernel", 3, 3,...

7.5CVSS7.3AI score0.00765EPSS
SaveExploits1References9
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•31 views

PT-2026-59865

Impact FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack: python import tensorflow as tf overlapping = True orig input = tf.constant.453409232,...

7.5CVSS7.2AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•27 views

PT-2026-59850

Impact When tf.quantization.fake quant with min max vars gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf import numpy as np arg 0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2,...

7.5CVSS7.2AI score0.00478EPSS
SaveExploits0References8
Positive Technologies
Positive Technologies
•added 2026/07/13 12:00 a.m.•72 views

PT-2026-59924

Impact The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. Patches We have patched the issue in GitHub commit...

9.1CVSS8.1AI score0.00492EPSS
SaveExploits0References8
Wolfi
Wolfi
•added 2026/07/11 2:16 a.m.•63 views

CVE-2026-49851 vulnerabilities

Vulnerabilities for packages: kubeflow-pipelines-visualization-server, jupyter-base-notebook...

8.7CVSS7.2AI score0.0052EPSS
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