TensorFlow is an end-to-end open source platform for machine learning. The API of tf.raw_ops.SparseCross allows combinations which would result in a CHECK-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type tstring which in fact contains integral elements. Fixing the type confusion by preventing mixing DT_STRING and DT_INT64 types solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
The product allocates or initializes a resource such as a pointer, object, or variable using one type, but it later accesses that resource using a type that is incompatible with the original type.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | 2.4.0 | * | |
| Tensorflow | 2.3.0 | * | |
| Tensorflow | 2.2.0 | * | |
| Tensorflow | * | * |
When the product accesses the resource using an incompatible type, this could trigger logical errors because the resource does not have expected properties. In languages without memory safety, such as C and C++, type confusion can lead to out-of-bounds memory access. While this weakness is frequently associated with unions when parsing data with many different embedded object types in C, it can be present in any application that can interpret the same variable or memory location in multiple ways. This weakness is not unique to C and C++. For example, errors in PHP applications can be triggered by providing array parameters when scalars are expected, or vice versa. Languages such as Perl, which perform automatic conversion of a variable of one type when it is accessed as if it were another type, can also contain these issues.