Understand vulnerabilities. Protect your applications.
Discover which vulnerabilities affect your technologies and how to fix them. Explore clear explanations, affected versions and practical recommendations - from CMS platforms and plugins to application libraries.
Vulnerability archive 443
Reset filters`CHECK_EQ` fail in `tf.raw_ops.TensorListResize`
Segfault in `tf.raw_ops.TensorListConcat`
`CHECK` fail in `BCast` overflow
Segfault via invalid attributes in `pywrap_tfe_src.cc`
FPE in `tf.image.generate_bounding_box_proposals`
Overflow in `tf.keras.losses.poisson`
Overflow in `ImageProjectiveTransformV2`
Overflow in `FusedResizeAndPadConv2D`
Seg fault in `ndarray_tensor_bridge` due to zero and large inputs
Out of bounds segmentation fault due to unequal op inputs in Tensorflow
TensorFlow vulnerable to `CHECK` fail in `Save` and `SaveSlices`
TensorFlow vulnerable to `CHECK` fail in `ParameterizedTruncatedNormal`
TensorFlow vulnerable to `CHECK` fail in `LRNGrad`
TensorFlow vulnerable to segfault in `RaggedBincount`
TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank`
TensorFlow vulnerable to `CHECK` fail in `MaxPool`
TensorFlow vulnerable to segfault in `SparseBincount`
TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`
TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`
TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`
TensorFlow vulnerable to segfault in `QuantizedMatMul`
TensorFlow vulnerable to segfault in `QuantizedBiasAdd`
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`
Know which vulnerabilities affect your application
With the DockRay integration and software inventory enabled, DockRay checks reported component versions against known vulnerabilities and notifies you about confirmed matches. Coverage depends on the supported technology and available advisory data.