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 =...

PYSEC-2026-977 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`

Impact If 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. python import tensorflow as tf numbits = 8 narrowrange = False inputs = tf.constant0, shape=2,3, dtype=tf.float32 min = tf.constant0,...

deepdefacer (>=0.0.2, <=1.0.4), delft (>=0.1.2, <=0.2.0) potentially affected by CVE-2022-35969 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-2022-35969 Source advisory: OSV:PYSEC-2026-1028...

deepdefacer (>=0.0.2, <=1.0.4), delft (>=0.1.2, <=0.2.0) potentially affected by CVE-2022-35968 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-2022-35968 Source advisory: OSV:PYSEC-2026-940...

deepdefacer (>=0.0.2, <=1.0.4), delft (>=0.1.2, <=0.2.0) potentially affected by CVE-2022-35982 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-2022-35982 Source advisory: OSV:PYSEC-2026-947...

niftynet (0.1.1rc58) potentially affected by CVE-2022-35969 via tensorflow-gpu (1.1.0)

tensorflow-gpu PYPI version 1.1.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - niftynet 0.1.1rc58 Source cves: CVE-2022-35969 Source advisory: OSV:PYSEC-2026-1028...

strawberryfields-gpu (>=0.7.3, <=0.10.0) potentially affected by CVE-2022-35969 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-2022-35969 Source advisory: OSV:PYSEC-2026-1028...

strawberryfields-gpu (>=0.7.3, <=0.10.0) potentially affected by CVE-2022-35982 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-2022-35982 Source advisory: OSV:PYSEC-2026-947...

PYSEC-2026-1038 TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 features = tf.constant28, shape=4,2, dtype=tf.quint8 minfeatures...

PYSEC-2026-959 TensorFlow vulnerable to segfault in `QuantizedMatMul`

Impact If QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxb It gives a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf Toutput = tf.qint32 transposea = False transposeb = False Tactivation = tf.quint8 a = tf.constant7,...

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-2026-947 TensorFlow vulnerable to segfault in `SparseBincount`

Impact If 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. python import tensorflow as tf binaryoutput = True indices = tf.random.uniformshape=, minval=-10000...

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,...

PYSEC-2026-948 TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

Impact If QuantizedBiasAdd is given mininput, maxinput, minbias, maxbias 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 outtype = tf.qint32 input = tf.constant85,170,255, shape=3, dtype=tf.quint8 bias =...

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...

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...

niftynet (0.1.1rc58) potentially affected by CVE-2022-35972 via tensorflow-gpu (1.1.0)

tensorflow-gpu PYPI version 1.1.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow-gpu and may be impacted: - niftynet 0.1.1rc58 Source cves: CVE-2022-35972 Source advisory: OSV:PYSEC-2026-948...

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...

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 `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 =...