CVE-2021-29522 (C++, published 2021-05-14T20:15Z)
Description: value: TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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.
Severity: LOW (CVSS v3 base score: 5.5)
Weakness: CWE-369 \u2014 Divide By Zero
The product divides a value by zero.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/core/kernels/conv_grad_ops_3d.cc
Commit message: Eliminate a division by 0 in 3D convolutions.

Also prevent a CHECK failed introduced in the most recent change.

PiperOrigin-RevId: 369322073
Change-Id: I4f609c028f89565fb2b49c3fdd20b63496582bae


Vulnerable code:

Fixed code:
    OP_REQUIRES(context, input_shape.dims() == 5,
                errors::InvalidArgument("input tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, filter_shape.dims() == 5,
        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, out_backprop_shape.dims() == 5,
        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
    OP_REQUIRES(context, input_shape.dims() == 5,
                errors::InvalidArgument("input tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, filter_shape.dims() == 5,
        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, out_backprop_shape.dims() == 5,
        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, work_unit_size > 0,
        errors::InvalidArgument("input, filter_sizes and out_backprop tensors "
                                "must all have at least 1 element"));

    OP_REQUIRES(context, input_shape.dims() == 5,
                errors::InvalidArgument("input tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, filter_shape.dims() == 5,
        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, out_backprop_shape.dims() == 5,
        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
    OP_REQUIRES(context, input_shape.dims() == 5,
                errors::InvalidArgument("input tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, filter_shape.dims() == 5,
        errors::InvalidArgument("filter_sizes tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, out_backprop_shape.dims() == 5,
        errors::InvalidArgument("out_backprop tensor must have 5 dimensions"));
    OP_REQUIRES(
        context, work_unit_size > 0,
        errors::InvalidArgument("input, filter_sizes and out_backprop tensors "
                                "must all have at least 1 element"));