TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to nullptr. This occurs whenever the dimensions of a or b are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
The product accesses or uses a pointer that has not been initialized.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | * | 2.4.4 (excluding) | |
| Tensorflow | 2.5.0 (including) | 2.5.2 (excluding) | |
| Tensorflow | 2.6.0 (including) | 2.6.0 (including) |
If the pointer contains an uninitialized value, then the value might not point to a valid memory location. This could cause the product to read from or write to unexpected memory locations, leading to a denial of service. If the uninitialized pointer is used as a function call, then arbitrary functions could be invoked. If an attacker can influence the portion of uninitialized memory that is contained in the pointer, this weakness could be leveraged to execute code or perform other attacks. Depending on memory layout, associated memory management behaviors, and product operation, the attacker might be able to influence the contents of the uninitialized pointer, thus gaining more fine-grained control of the memory location to be accessed.