TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using tf.raw_ops.NonMaxSuppressionV5 by triggering a division by 0. The implementation uses a user controlled argument to resize a std::vector. However, as std::vector::resize takes the size argument as a size_t and output_size is an int, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in CombinedNonMaxSuppression. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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) |