TensorFlow is an open source platform for machine learning. The UnbatchGradOp function takes an argument id that is assumed to be a scalar. A nonscalar id can trigger a CHECK failure and crash the program. It also requires its argument batch_index to contain three times the number of elements as indicated in its batch_index.dim_size(0). An incorrect batch_index can trigger a CHECK failure and crash the program. We have patched the issue in GitHub commit 5f945fc6409a3c1e90d6970c9292f805f6e6ddf2. 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 | 2.7.0 (including) | 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.