chore: import upstream snapshot with attribution

tensorflow/security/advisory/tfsa-2021-031.md

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+## TFSA-2021-031: `CHECK`-fail in `QuantizeAndDequantizeV4Grad`
+
+### CVE Number
+CVE-2021-29544
+
+### Impact
+An attacker can trigger a denial of service via a `CHECK`-fail in
+`tf.raw_ops.QuantizeAndDequantizeV4Grad`:
+
+```python
+import tensorflow as tf
+
+gradient_tensor = tf.constant([0.0], shape=[1])
+input_tensor = tf.constant([0.0], shape=[1])
+input_min = tf.constant([[0.0]], shape=[1, 1])
+input_max = tf.constant([[0.0]], shape=[1, 1])
+
+tf.raw_ops.QuantizeAndDequantizeV4Grad(
+  gradients=gradient_tensor, input=input_tensor,
+  input_min=input_min, input_max=input_max, axis=0)
+```
+
+This is because the
+[implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163)
+does not validate the rank of the `input_*` tensors. In turn, this results in
+the tensors being passes as they are to
+[`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306):
+
+```cc
+template <typename Device, typename T>
+struct QuantizeAndDequantizePerChannelGradientImpl {
+  static void Compute(const Device& d,
+                      typename TTypes<T, 3>::ConstTensor gradient,
+                      typename TTypes<T, 3>::ConstTensor input,
+                      const Tensor* input_min_tensor,
+                      const Tensor* input_max_tensor,
+                      typename TTypes<T, 3>::Tensor input_backprop,
+                      typename TTypes<T>::Flat input_min_backprop,
+                      typename TTypes<T>::Flat input_max_backprop) {
+    ...
+    auto input_min = input_min_tensor->vec<T>();
+    auto input_max = input_max_tensor->vec<T>();
+    ...
+}
+```
+
+However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK`
+failure otherwise.
+
+### Patches
+We have patched the issue in GitHub commit
+[20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b).
+
+The fix will be included in TensorFlow 2.5.0. We will also cherrypick this
+commit on TensorFlow 2.4.2 as this is the only other affected version.
+
+### 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.