TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in boosted_trees_create_quantile_stream_resource by using negative arguments. The implementation does not validate that num_streams only contains non-negative numbers. In turn, this results in using this value to allocate memory. However, reserve receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
When converting from one data type to another, such as long to integer, data can be omitted or translated in a way that produces unexpected values. If the resulting values are used in a sensitive context, then dangerous behaviors may occur.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | 2.3.0 (including) | 2.3.4 (excluding) | |
| Tensorflow | 2.4.0 (including) | 2.4.3 (excluding) | |
| Tensorflow | 2.5.0 (including) | 2.5.0 (including) | |
| Tensorflow | 2.6.0-rc0 (including) | 2.6.0-rc0 (including) | |
| Tensorflow | 2.6.0-rc1 (including) | 2.6.0-rc1 (including) | |
| Tensorflow | 2.6.0-rc2 (including) | 2.6.0-rc2 (including) |