GHSA-qfpc-5pjr-mh26 Missing validation in shape inference for `Dequantize`

• PyPI tensorflow: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-cpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-cpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-gpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-gpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1

Impact The shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments: ```python import tensorflow as tf tf.compat.v1.disable_v2_behavior() tf.raw_ops.Dequantize( input_tensor = tf.constant(-10.0, dtype=tf.float32), input_tensor = tf.cast(input_tensor, dtype=tf.quint8), min_range = tf.constant([], shape=[0], dtype=tf.float32), max_range = tf.constant([], shape=[0], dtype=tf.float32), mode = 'MIN_COMBINED', narrow_range=False, axis=-10, dtype=tf.dtypes.float32) ``` The shape inference implementation uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. Patches We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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. 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 of Baidu Security.