TensorFlow vulnerable to segfault in `SparseBincount`

ImpactIf SparseBincount is given inputs for indices, values, and denseshape that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfbinaryoutput = Trueindices = tf.random.uniformshape=, minval=-10000,...

TensorFlow vulnerable to segfault in `RaggedBincount`

ImpactIf RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfbinaryoutput = Truesplits = tf.random.uniformshape=0, minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430values =...

TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`

ImpactThe implementation of AvgPoolGrad does not fully validate the input originputshape. This results in a CHECK failure which can be used to trigger a denial of service attack:pythonimport tensorflow as tfksize = 1, 2, 2, 1strides = 1, 2, 2, 1padding = "VALID"dataformat = "NHWC"originputshape =...

TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`

ImpactIf QuantizedInstanceNorm is given xmin or xmax tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfoutputrangegiven = Falsegivenymin = 0givenymax = 0varianceepsilon = 1e-05minseparation = 0.001x =...

TensorFlow vulnerable to segfault in `QuantizedAdd`

ImpactIf QuantizedAdd is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfToutput = tf.qint32x = tf.constant140, shape=1, dtype=tf.quint8y = tf.constant26, shape=10, dtype=tf.quint8mi...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`

ImpactIf FakeQuantWithMinMaxVars is given min or max tensors of a nonzero rank, it results in a CHECK fail that can be used to trigger a denial of service attack.pythonimport tensorflow as tfnumbits = 8narrowrange = Falseinputs = tf.constant0, shape=2,3, dtype=tf.float32min = tf.constant0,...

nuclear-python (>=0.14.4.1, <=0.14.6.2) potentially affected by CVE-2022-35972 via tensorflow-gpu (1.13.2)

tensorflow-gpu PYPI version 1.13.2 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - nuclear-python =0.14.4.1, =0.14.6.2 Source cves: CVE-2022-35972 Source advisory: OSV:PYSEC-2026-948...

strawberryfields-gpu (>=0.7.3, <=0.10.0) potentially affected by CVE-2022-35972 via tensorflow-gpu (1.6.0)

tensorflow-gpu PYPI version 1.6.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - strawberryfields-gpu =0.7.3, =0.10.0 Source cves: CVE-2022-35972 Source advisory: OSV:PYSEC-2026-948...

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`

ImpactIf QuantizeDownAndShrinkRange is given nonscalar inputs for inputmin or inputmax, it results in a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfouttype = tf.quint8input = tf.constant1, shape=3, dtype=tf.qint32inputmin = tf.constant, shape=0,...

deepdefacer (>=0.0.2, <=1.0.4), delft (>=0.1.2, <=0.2.0) potentially affected by CVE-2022-35972 via tensorflow-gpu (1.8.0)

tensorflow-gpu PYPI version 1.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - deepdefacer =0.0.2, =0.1.2, =0.2.0 Source cves: CVE-2022-35972 Source advisory: OSV:PYSEC-2026-948...

PYSEC-2026-938 TensorFlow vulnerable to segfault in `RaggedBincount`

Impact If RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf binaryoutput = True splits = tf.random.uniformshape=0, minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430 values =...

PYSEC-2026-1041 TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

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 originput = tf.constant.453409232,...

PYSEC-2026-1028 TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`

Impact The implementation of Conv2DBackpropInput requires inputsizes 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" usecudnnongpu = True explicitpaddings =...

TensorFlow vulnerable to segfault in `QuantizedMatMul`

ImpactIf QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxbIt gives a segfault that can be used to trigger a denial of service attack.pythonimport tensorflow as tfToutput = tf.qint32transposea = Falsetransposeb = FalseTactivation = tf.quint8a = tf.constant7, shape=3,4,...

PYSEC-2026-1037 TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact If QuantizedAdd is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf Toutput = tf.qint32 x = tf.constant140, shape=1, dtype=tf.quint8 y = tf.constant26, shape=10,...

PYSEC-2026-980 TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank`

Impact When tf.linalg.matrixrank receives an empty input a, the GPU kernel gives a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf a = tf.constant, shape=0, 1, 1, dtype=tf.float32 tf.linalg.matrixranka=a Patches We have patched the issue in GitHub...

causalegm (>=0.2.1, <=0.2.5), chrombpnet (>=0.1.0, <=0.1.2) +3 more potentially affected by CVE-2022-35974 via tensorflow-gpu (2.8.0)

tensorflow-gpu PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - causalegm =0.2.1, =0.1.0, =0.0.6, =2.3.5, =2.4.1 - tlaunch 0.0.2 Source cves: CVE-2022-35974 Source advisory: OSV:PYSEC-2026-1039...

classitransformers (0.0.1), pyclue (>=0.1.0, <=0.1.2) +4 more potentially affected by CVE-2022-35974 via tensorflow-gpu (1.15.2)

tensorflow-gpu PYPI version 1.15.2 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - classitransformers 0.0.1 - pyclue =0.1.0, =0.1.1, =0.1.2, =0.1.2, =0.1.3 Source cves: CVE-2022-35974 Source advisory:...

jlpyutils (>=0.0.7, <=0.1.1) potentially affected by CVE-2022-35974 via tensorflow-gpu (1.12.2)

tensorflow-gpu PYPI version 1.12.2 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - jlpyutils =0.0.7, =0.1.1 Source cves: CVE-2022-35974 Source advisory: OSV:PYSEC-2026-1039...

arekit (>=0.21.0, <=0.22.1), arenets (>=0.23.0, <=0.23.1) +19 more potentially affected by CVE-2022-35974 via tensorflow-gpu (1.14.0)

tensorflow-gpu PYPI version 1.14.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - arekit =0.21.0, =0.23.0, =1.0.0, =0.0.1, =0.1.6, =0.1.0, =0.0.6, =1.0.1, =1.0.2 - ocrd-anybaseocr 0.0.1 and more Source cves:...