PT-2026-59902

Impact When running with XLA, tf.raw ops.ParallelConcat segfaults with a nullptr dereference when given a parameter shape with rank that is not greater than zero. python import tensorflow as tf func = tf.raw ops.ParallelConcat para = 'shape': 0, 'values': 1 @tf.functionjit compile=True def test: ...

PT-2026-59890

Impact When tf.raw ops.ImageProjectiveTransformV2 is given a large output shape, it overflows. python import tensorflow as tf interpolation = "BILINEAR" fill mode = "REFLECT" images = tf.constant0.184634328, shape=2,5,8,3, dtype=tf.float32 transforms = tf.constant0.378575385, shape=2,8,...

PT-2026-59748

Impact When running with XLA, tf.raw ops.Bincount segfaults when given a parameter weights that is neither the same shape as parameter arr nor a length-0 tensor. python import tensorflow as tf func = tf.raw ops.Bincount para='arr': 6, 'size': 804, 'weights': 52, 351 @tf.functionjit compile=True d...

PT-2026-59898

Impact If tf.raw ops.TensorListConcat is given element shape=, it results segmentation fault which can be used to trigger a denial of service attack. python import tensorflow as tf tf.raw ops.TensorListConcat input handle=tf.data.experimental.to varianttf.data.Dataset.from tensor slices1, 2, 3,...

PT-2026-59750

Impact The implementation of FractionalAvgPoolGrad does not fully validate the input orig input tensor shape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack. python import tensorflow as tf overlapping = True orig input tensor...

PT-2026-59988

Impact When TensorListScatter and TensorListScatterV2 receive an element shape of a rank greater than one, they give a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=2, 2, 2, dtype=tf.float16, maxval=None arg...

PT-2026-59508

Local file inclusion LFI and server-side request forgery SSRF vulnerabilities in pgAdmin 4 LLM API configuration endpoints. User-supplied api key file and api url preferences were passed to the LLM provider clients without validation. An authenticated user could read arbitrary server-side files b...

PT-2026-59917

Impact If LRNGrad is given an output image input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf depth radius = 1 bias = 1.59018219 alpha = 0.117728651 beta = 0.404427052 input grads =...

PT-2026-59747

Impact TFversion 2.11.0 //tensorflow/core/ops/array ops.cc:1067 const Tensor hypothesis shape t = c-input tensor2; std::vector dimshypothesis shape t-NumElements - 1; for int i = 0; i MakeDimstd::maxh valuesi, t valuesi; if hypothesis shape t is empty, hypothesis shape t-NumElements - 1 will be...

PT-2026-59931

Impact version:2.11.0 //core/ops/audio ops.cc:70 Status SpectrogramShapeFnInferenceContext c ShapeHandle input; TF RETURN IF ERRORc-WithRankc-input0, 2, &input; int32 t window size; TF RETURN IF ERRORc-GetAttr"window size", &window size; int32 t stride; TF RETURN IF ERRORc-GetAttr"stride", &strid...

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,...

PT-2026-59925

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg 1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=No...

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 =...

PT-2026-59959

Impact 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. E.g. the following raises an error: python np.ones0, 231, 231 An example of a proof of concept: python import numpy as np import tensorflow as tf input val ...

PYSEC-2026-3350 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 =...

PYSEC-2026-3211 Type confusion leading to segfault in Tensorflow

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion: python import tensorflow as tf @tf.function def test: y = tf.rawops.ConcatV2 values=1,2,3,4,5,6, axis = 0xb500005b return y test The axis argument...

PYSEC-2026-3240 Integer overflows in Tensorflow

Impact The implementations of SparseCwise ops are vulnerable to integer overflows. These can be used to trigger large allocations so, OOM based denial of service or CHECK-fails when building new TensorShape objects so, assert failures based denial of service: python import tensorflow as tf import...

PYSEC-2026-3204 Reachable Assertion in Tensorflow

Impact When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a CHECK assertion is invalidated based on user controlled arguments, if the tensors have an invalid dtype and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow...

PYSEC-2026-3241 Crash when type cannot be specialized in Tensorflow

Impact Under certain scenarios, TensorFlow can fail to specialize a type during shape inference: cc void InferenceContext::PreInputInit const OpDef& opdef, const std::vector& inputtensors, const std::vector& inputtensorsasshapes const auto ret = fulltype::SpecializeTypeattrs, opdef;...

PYSEC-2026-3121 Out of bounds read in Tensorflow

Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batchdim and can result in a heap OOB read: python import tensorflow as tf @tf.function def test: y = tf.rawops.ReverseSequence input = 'aaa','bbb', seqlengths = 1,1,1, seqdim = -10, batchdim = -...