Vulnerability: Heap OOB access in `Dilation2DBackpropInput` ### Impact
An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`:

```python
import tensorflow as tf
    
input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32)
out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32)

tf.raw_ops.Dilation2DBackpropInput(
  input=input_tensor, filter=filter, out_backprop=out_backprop, 
  strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID')
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array.
  
```cc               
in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d);
```                 
    
The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`.

### Patches
We have patched the issue in GitHub commit [3f6fe4dfef6f57e768260b48166c27d148f3015f](https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f).

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.
Vulnerability ID: GHSA-pvrc-hg3f-58r6
CWE: CWE-787
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/core/kernels/dilation_ops.cc
Commit message: Add missing validations in dillation ops.

PiperOrigin-RevId: 372037158
Change-Id: I4ee304c84a02550c030288a6534000b934fc1599
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 class DilationOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);      // Output tensor is of the following dimensions:     // [ batch, out_rows, out_cols, depth ] class DilationBackpropInputOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);      // Verify that the incoming gradient tensor has the expected size     // [ batch, out_rows, out_cols, depth ] struct DilationBackpropInput<CPUDevice, T> {                 }               }             }             in_backprop(b, h_in_max, w_in_max, d) +=                 out_backprop(b, h_out, w_out, d);           }         }       } class DilationBackpropFilterOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);      // Verify that the incoming gradient tensor has the expected size     // [ batch, out_rows, out_cols, depth ] struct DilationBackpropFilter<CPUDevice, T> {                 }               }             }             filter_backprop(h_max, w_max, d) +=                 out_backprop(b, h_out, w_out, d);           }         }       }

Fixed version (after fix):
 class DilationOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);     if (!context->status().ok()) return;      // Output tensor is of the following dimensions:     // [ batch, out_rows, out_cols, depth ] class DilationBackpropInputOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);     if (!context->status().ok()) return;      // Verify that the incoming gradient tensor has the expected size     // [ batch, out_rows, out_cols, depth ] struct DilationBackpropInput<CPUDevice, T> {                 }               }             }             if (h_in_max < input_rows && w_in_max < input_cols) {               in_backprop(b, h_in_max, w_in_max, d) +=                   out_backprop(b, h_out, w_out, d);             }           }         }       } class DilationBackpropFilterOp : public OpKernel {     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,                &out_cols);     if (!context->status().ok()) return;      // Verify that the incoming gradient tensor has the expected size     // [ batch, out_rows, out_cols, depth ] struct DilationBackpropFilter<CPUDevice, T> {                 }               }             }             if (h_max < filter_rows && w_max < filter_cols) {               filter_backprop(h_max, w_max, d) +=                   out_backprop(b, h_out, w_out, d);             }           }         }       }

Patch:
patch: @@ -130,6 +130,7 @@ class DilationOp : public OpKernel {
     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,
                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,
                &out_cols);
+    if (!context->status().ok()) return;
 
     // Output tensor is of the following dimensions:
     // [ batch, out_rows, out_cols, depth ]
@@ -229,6 +230,7 @@ class DilationBackpropInputOp : public OpKernel {
     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,
                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,
                &out_cols);
+    if (!context->status().ok()) return;
 
     // Verify that the incoming gradient tensor has the expected size
     // [ batch, out_rows, out_cols, depth ]
@@ -318,8 +320,10 @@ struct DilationBackpropInput<CPUDevice, T> {
                 }
               }
             }
-            in_backprop(b, h_in_max, w_in_max, d) +=
-                out_backprop(b, h_out, w_out, d);
+            if (h_in_max < input_rows && w_in_max < input_cols) {
+              in_backprop(b, h_in_max, w_in_max, d) +=
+                  out_backprop(b, h_out, w_out, d);
+            }
           }
         }
       }
@@ -349,6 +353,7 @@ class DilationBackpropFilterOp : public OpKernel {
     ParseSizes(context, strides_, rates_, padding_, &stride_rows, &stride_cols,
                &rate_rows, &rate_cols, &pad_top, &pad_left, &out_rows,
                &out_cols);
+    if (!context->status().ok()) return;
 
     // Verify that the incoming gradient tensor has the expected size
     // [ batch, out_rows, out_cols, depth ]
@@ -438,8 +443,10 @@ struct DilationBackpropFilter<CPUDevice, T> {
                 }
               }
             }
-            filter_backprop(h_max, w_max, d) +=
-                out_backprop(b, h_out, w_out, d);
+            if (h_max < filter_rows && w_max < filter_cols) {
+              filter_backprop(h_max, w_max, d) +=
+                  out_backprop(b, h_out, w_out, d);
+            }
           }
         }
       }
path: tensorflow/core/kernels/dilation_ops.cc