In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Truncation errors occur when a primitive is cast to a primitive of a smaller size and data is lost in the conversion.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | * | 1.15.4 (excluding) | |
| Tensorflow | 2.0.0 (including) | 2.0.3 (excluding) | |
| Tensorflow | 2.1.0 (including) | 2.1.2 (excluding) | |
| Tensorflow | 2.2.0 (including) | 2.2.1 (excluding) | |
| Tensorflow | 2.3.0 (including) | 2.3.1 (excluding) |