Vulnerability: Heap OOB read in TFLite's implementation of `Minimum` or `Maximum` ### Impact
The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty.

This is because [the broadcasting implementation](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds:

```cc
auto maxmin_func = [&](int indexes[N]) {
  output_data[SubscriptToIndex(output_desc, indexes)] =
    op(input1_data[SubscriptToIndex(desc1, indexes)],
        input2_data[SubscriptToIndex(desc2, indexes)]);
};
```

### Patches
We have patched the issue in GitHub commit [953f28dca13c92839ba389c055587cfe6c723578](https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578).

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 members of the Aivul Team from Qihoo 360.
Vulnerability ID: GHSA-24x6-8c7m-hv3f
CWE: CWE-125
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/lite/kernels/maximum_minimum.cc
Commit message: Prevent a null pointer exception in TFLite

PiperOrigin-RevId: 370800206
Change-Id: Idd437ebce4ff224120d8eefc1c14c062173b71d6
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 template <KernelType kernel_type, typename OpType> TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {   OpContext op_context(context, node);      switch (op_context.output->type) {       case kTfLiteFloat32:         TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);         break;       case kTfLiteUInt8:         TFLiteOperation<kernel_type, uint8_t, OpType>(context, node,                                                       op_context);         break;       case kTfLiteInt8:         TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);         break;       case kTfLiteInt32:         TFLiteOperation<kernel_type, int32_t, OpType>(context, node,                                                       op_context);         break;       case kTfLiteInt64:         TFLiteOperation<kernel_type, int64_t, OpType>(context, node,                                                       op_context);         break;       case kTfLiteInt16:         TFLiteOperation<kernel_type, int16_t, OpType>(context, node,                                                       op_context);         break;       default:         context->ReportError(context,                              "Type %d is currently not supported by Maximum.",                              op_context.output->type);         return kTfLiteError;     }   return kTfLiteOk; } 

Fixed version (after fix):
 template <KernelType kernel_type, typename OpType> TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {   OpContext op_context(context, node);    // If inputs have no element, shortcircuit.   if (NumElements(op_context.input1) == 0 ||       NumElements(op_context.input2) == 0) {     return kTfLiteOk;   }    switch (op_context.output->type) {     case kTfLiteFloat32:       TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);       break;     case kTfLiteUInt8:       TFLiteOperation<kernel_type, uint8_t, OpType>(context, node, op_context);       break;     case kTfLiteInt8:       TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);       break;     case kTfLiteInt32:       TFLiteOperation<kernel_type, int32_t, OpType>(context, node, op_context);       break;     case kTfLiteInt64:       TFLiteOperation<kernel_type, int64_t, OpType>(context, node, op_context);       break;     case kTfLiteInt16:       TFLiteOperation<kernel_type, int16_t, OpType>(context, node, op_context);       break;     default:       context->ReportError(context,                            "Type %d is currently not supported by Maximum.",                            op_context.output->type);       return kTfLiteError;   }   return kTfLiteOk; } 

Patch:
patch: @@ -157,35 +157,37 @@ template <KernelType kernel_type, typename OpType>
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   OpContext op_context(context, node);
 
-    switch (op_context.output->type) {
-      case kTfLiteFloat32:
-        TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);
-        break;
-      case kTfLiteUInt8:
-        TFLiteOperation<kernel_type, uint8_t, OpType>(context, node,
-                                                      op_context);
-        break;
-      case kTfLiteInt8:
-        TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);
-        break;
-      case kTfLiteInt32:
-        TFLiteOperation<kernel_type, int32_t, OpType>(context, node,
-                                                      op_context);
-        break;
-      case kTfLiteInt64:
-        TFLiteOperation<kernel_type, int64_t, OpType>(context, node,
-                                                      op_context);
-        break;
-      case kTfLiteInt16:
-        TFLiteOperation<kernel_type, int16_t, OpType>(context, node,
-                                                      op_context);
-        break;
-      default:
-        context->ReportError(context,
-                             "Type %d is currently not supported by Maximum.",
-                             op_context.output->type);
-        return kTfLiteError;
-    }
+  // If inputs have no element, shortcircuit.
+  if (NumElements(op_context.input1) == 0 ||
+      NumElements(op_context.input2) == 0) {
+    return kTfLiteOk;
+  }
+
+  switch (op_context.output->type) {
+    case kTfLiteFloat32:
+      TFLiteOperation<kernel_type, float, OpType>(context, node, op_context);
+      break;
+    case kTfLiteUInt8:
+      TFLiteOperation<kernel_type, uint8_t, OpType>(context, node, op_context);
+      break;
+    case kTfLiteInt8:
+      TFLiteOperation<kernel_type, int8_t, OpType>(context, node, op_context);
+      break;
+    case kTfLiteInt32:
+      TFLiteOperation<kernel_type, int32_t, OpType>(context, node, op_context);
+      break;
+    case kTfLiteInt64:
+      TFLiteOperation<kernel_type, int64_t, OpType>(context, node, op_context);
+      break;
+    case kTfLiteInt16:
+      TFLiteOperation<kernel_type, int16_t, OpType>(context, node, op_context);
+      break;
+    default:
+      context->ReportError(context,
+                           "Type %d is currently not supported by Maximum.",
+                           op_context.output->type);
+      return kTfLiteError;
+  }
   return kTfLiteOk;
 }
path: tensorflow/lite/kernels/maximum_minimum.cc