TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since vector_num_elements is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
The product divides a value by zero.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | * | 2.1.4 (excluding) | |
| Tensorflow | 2.2.0 (including) | 2.2.3 (excluding) | |
| Tensorflow | 2.3.0 (including) | 2.3.3 (excluding) | |
| Tensorflow | 2.4.0 (including) | 2.4.2 (excluding) |