CVE-2021-29538: Divide By Zero (CWE-369)
value: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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.
CWE description: The product divides a value by zero.
CVSS v3 base score: 5.5
Published: 2021-05-14T20:15Z
Repository: https://github.com/tensorflow/tensorflow
Language: C++
Files changed: tensorflow/core/kernels/conv_grad_filter_ops.cc
Commit date: 2021-04-21 17:56:05 -0700
Commit message: Fix issues in Conv2DBackpropFilter.

PiperOrigin-RevId: 369772454
Change-Id: I49b465f2ae2ce91def61b56cea8000197d5177d8

Diff: diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc
index fb48e3e285a27..2645d850ab7cf 100644
--- a/tensorflow/core/kernels/conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc
@@ -493,10 +493,18 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
 
     // The total dimension size of each kernel.
     const int filter_total_size = dims.spatial_dims[0].filter_size *
                                   dims.spatial_dims[1].filter_size *
                                   dims.in_depth;
+    OP_REQUIRES(
+        context,
+        filter_total_size * dims.out_depth == filter_backprop->NumElements(),
+        errors::InvalidArgument(
+            "filter_size does not have enough elements, requested ",
+            filter_total_size * dims.out_depth, ", got ",
+            filter_backprop->NumElements()));
+
     // The output image size is the spatial size of the output.
     const int output_image_size =
         dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size;
 
     // Shard 'batch' images into 'shard_size' groups of images to be fed
@@ -516,10 +524,15 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
 
     const size_t size_C = filter_total_size * dims.out_depth;
 
     const size_t work_unit_size = size_A + size_B + size_C;
 
+    OP_REQUIRES(
+        context, work_unit_size != 0,
+        errors::InvalidArgument(
+            "Work size for convolution would be 0, which is not acceptable"));
+
     const size_t shard_size =
         (target_working_set_size + work_unit_size - 1) / work_unit_size;
 
     Tensor col_buffer;
     OP_REQUIRES_OK(context,

Fixed code:     OP_REQUIRES(
        context,
        filter_total_size * dims.out_depth == filter_backprop->NumElements(),
        errors::InvalidArgument(
            "filter_size does not have enough elements, requested ",
            filter_total_size * dims.out_depth, ", got ",
            filter_backprop->NumElements()));

    OP_REQUIRES(
        context, work_unit_size != 0,
        errors::InvalidArgument(
            "Work size for convolution would be 0, which is not acceptable"));