TensorFlow is an open source platform for machine learning. If LRNGrad is given an output_image input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | * | 2.7.2 (excluding) | |
| Tensorflow | 2.8.0 (including) | 2.8.1 (excluding) | |
| Tensorflow | 2.9.0 (including) | 2.9.1 (excluding) | |
| Tensorflow | 2.10-rc0 (including) | 2.10-rc0 (including) | |
| Tensorflow | 2.10-rc1 (including) | 2.10-rc1 (including) | |
| Tensorflow | 2.10-rc2 (including) | 2.10-rc2 (including) | |
| Tensorflow | 2.10-rc3 (including) | 2.10-rc3 (including) |
While assertion is good for catching logic errors and reducing the chances of reaching more serious vulnerability conditions, it can still lead to a denial of service. For example, if a server handles multiple simultaneous connections, and an assert() occurs in one single connection that causes all other connections to be dropped, this is a reachable assertion that leads to a denial of service.