Commit message: Prevent overflow in sparse op

PiperOrigin-RevId: 372442006
Change-Id: I60fe31cd7e56fb3501e97c63500caf902ddeee96
CVE: CVE-2021-29584
CWE: CWE-369
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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.

Project: tensorflow
File: sparse_split_op.cc

  void Compute(OpKernelContext* context) override {
    const int64 axis_input = context->input(0).scalar<int64>()();
    const Tensor& input_indices = context->input(1);
    const Tensor& input_values = context->input(2);
    const Tensor& input_shape = context->input(3);

    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices.shape()),
                errors::InvalidArgument(
                    "Input indices should be a matrix but received shape ",
                    input_indices.shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values.shape()),
                errors::InvalidArgument(
                    "Input values should be a vector but received shape ",
                    input_indices.shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape.shape()),
                errors::InvalidArgument(
                    "Input shape should be a vector but received shape ",
                    input_shape.shape().DebugString()));

    const int64 input_rank = input_shape.vec<int64>().size();
    const int64 axis = (axis_input < 0) ? input_rank + axis_input : axis_input;

    OP_REQUIRES(
        context, axis >= 0 && axis < input_rank,
        errors::InvalidArgument("Input axis should be in range [", -input_rank,
                                ", ", input_rank, "), got ", axis_input));

    OP_REQUIRES(context,
                num_split_ >= 1 && num_split_ <= input_shape.vec<int64>()(axis),
                errors::InvalidArgument("Input num_split should be between 1 "
                                        "and the splitting dimension size (",
                                        input_shape.vec<int64>()(axis),
                                        "), got ", num_split_));

    sparse::SparseTensor sparse_tensor;
    OP_REQUIRES_OK(context,
                   sparse::SparseTensor::Create(
                       input_indices, input_values,
                       TensorShape(input_shape.vec<int64>()), &sparse_tensor));

    std::vector<sparse::SparseTensor> outputs;
    OP_REQUIRES_OK(context, sparse::SparseTensor::Split<T>(
                                sparse_tensor, axis, num_split_, &outputs));

    for (int slice_index = 0; slice_index < num_split_; ++slice_index) {
      context->set_output(slice_index, outputs[slice_index].indices());
      context->set_output(slice_index + num_split_,
                          outputs[slice_index].values());
      Tensor* shape = nullptr;
      OP_REQUIRES_OK(context, context->allocate_output(
                                  slice_index + 2 * num_split_,
                                  {outputs[slice_index].dims()}, &shape));
      auto output_shape = outputs[slice_index].shape();
      for (int dim = 0; dim < outputs[slice_index].dims(); ++dim) {
        shape->vec<int64>()(dim) = output_shape[dim];
      }
    }
  }