chore: import upstream snapshot with attribution

tensorflow/security/advisory/tfsa-2021-058.md

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+## TFSA-2021-058: Heap out of bounds read in `RequantizationRange`
+
+### CVE Number
+CVE-2021-29569
+
+### Impact
+The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside
+of bounds of heap allocated data if attacker supplies specially crafted inputs:
+
+```python
+import tensorflow as tf
+
+input = tf.constant([1], shape=[1], dtype=tf.qint32)
+input_max = tf.constant([], dtype=tf.float32)
+input_min = tf.constant([], dtype=tf.float32)
+
+tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)
+```
+
+The
+[implementation](https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50)
+assumes that the `input_min` and `input_max` tensors have at least one element,
+as it accesses the first element in two arrays:
+
+```cc
+const float input_min_float = ctx->input(1).flat<float>()(0);
+const float input_max_float = ctx->input(2).flat<float>()(0);
+```
+
+If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty
+array. Hence, accesing even the 0th element is a read outside the bounds.
+
+### Patches
+We have patched the issue in GitHub commit
+[ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).
+
+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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for
+more information regarding the security model and how to contact us with issues
+and questions.
+
+### Attribution
+This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu
+X-Team.