chore: import upstream snapshot with attribution

tensorflow/security/advisory/tfsa-2021-037.md

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+## TFSA-2021-037: Division by 0 in `QuantizedAdd`
+
+### CVE Number
+CVE-2021-29549
+
+### Impact
+An attacker can cause a runtime division by zero error and denial of service in
+`tf.raw_ops.QuantizedAdd`:
+
+```python
+import tensorflow as tf
+
+x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8)
+y = tf.constant([], shape=[2, 0], dtype=tf.quint8)
+
+min_x = tf.constant(10.723421015884028)
+max_x = tf.constant(15.19578006631113)
+min_y = tf.constant(-5.539003866682977)
+max_y = tf.constant(42.18819949559947)
+
+tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
+```
+
+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.
+
+```cc
+void VectorTensorAddition(const T* vector_data, float min_vector,
+                          float max_vector, int64 vector_num_elements,
+                          const T* tensor_data, float min_tensor,
+                          float max_tensor, int64 tensor_num_elements,
+                          float output_min, float output_max, Toutput* output) {
+  for (int i = 0; i < tensor_num_elements; ++i) {
+    const int64 vector_i = i % vector_num_elements;
+    ...
+  }
+}
+```
+
+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.
+
+### Patches
+We have patched the issue in GitHub commit
+[744009c9e5cc5d0447f0dc39d055f917e1fd9e16](https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16).
+
+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 Yakun Zhang and Ying Wang of Baidu
+X-Team.