Commit message: Validate that a and b are proper sparse tensors

PiperOrigin-RevId: 373248068
Change-Id: I0a2041a0747901b3f00387a6a3bce9bca6b0b3b1
CVE: CVE-2021-29609
CWE: CWE-787
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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_add_op.cc

  void Compute(OpKernelContext *ctx) override {
    // (0) validations
    const Tensor *a_indices, *b_indices, *a_values_t, *b_values_t, *a_shape,
        *b_shape, *thresh_t;

    OP_REQUIRES_OK(ctx, ctx->input("a_indices", &a_indices));
    OP_REQUIRES_OK(ctx, ctx->input("b_indices", &b_indices));
    OP_REQUIRES(ctx,
                TensorShapeUtils::IsMatrix(a_indices->shape()) &&
                    TensorShapeUtils::IsMatrix(b_indices->shape()),
                errors::InvalidArgument(
                    "Input indices should be matrices but received shapes: ",
                    a_indices->shape().DebugString(), " and ",
                    b_indices->shape().DebugString()));
    const int64 a_nnz = a_indices->dim_size(0);
    const int64 b_nnz = b_indices->dim_size(0);

    OP_REQUIRES_OK(ctx, ctx->input("a_values", &a_values_t));
    OP_REQUIRES_OK(ctx, ctx->input("b_values", &b_values_t));

    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(a_values_t->shape()) &&
                    TensorShapeUtils::IsVector(b_values_t->shape()),
                errors::InvalidArgument(
                    "Input values should be vectors but received shapes: ",
                    a_values_t->shape().DebugString(), " and ",
                    b_values_t->shape().DebugString()));
    auto a_values = ctx->input(1).vec<T>();
    auto b_values = ctx->input(4).vec<T>();
    OP_REQUIRES(
        ctx, a_values.size() == a_nnz && b_values.size() == b_nnz,
        errors::InvalidArgument("Expected ", a_nnz, " and ", b_nnz,
                                " non-empty input values, got ",
                                a_values.size(), " and ", b_values.size()));

    OP_REQUIRES_OK(ctx, ctx->input("a_shape", &a_shape));
    OP_REQUIRES_OK(ctx, ctx->input("b_shape", &b_shape));
    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(a_shape->shape()) &&
                    TensorShapeUtils::IsVector(b_shape->shape()),
                errors::InvalidArgument(
                    "Input shapes should be a vector but received shapes ",
                    a_shape->shape().DebugString(), " and ",
                    b_shape->shape().DebugString()));
    OP_REQUIRES(
        ctx, a_shape->IsSameSize(*b_shape),
        errors::InvalidArgument(
            "Operands do not have the same ranks; got shapes: ",
            a_shape->SummarizeValue(10), " and ", b_shape->SummarizeValue(10)));
    const auto a_shape_flat = a_shape->flat<int64>();
    const auto b_shape_flat = b_shape->flat<int64>();
    for (int i = 0; i < a_shape->NumElements(); ++i) {
      OP_REQUIRES(ctx, a_shape_flat(i) == b_shape_flat(i),
                  errors::InvalidArgument(
                      "Operands' shapes do not match: got ", a_shape_flat(i),
                      " and ", b_shape_flat(i), " for dimension ", i));
    }

    OP_REQUIRES_OK(ctx, ctx->input("thresh", &thresh_t));
    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(thresh_t->shape()),
                errors::InvalidArgument(
                    "The magnitude threshold must be a scalar: got shape ",
                    thresh_t->shape().DebugString()));
    // std::abs() so that it works for complex{64,128} values as well
    const Treal thresh = thresh_t->scalar<Treal>()();

    // (1) do a pass over inputs, and append values and indices to vectors
    auto a_indices_mat = a_indices->matrix<int64>();
    auto b_indices_mat = b_indices->matrix<int64>();
    std::vector<std::pair<bool, int64>> entries_to_copy;  // from_a?, idx
    entries_to_copy.reserve(a_nnz + b_nnz);
    std::vector<T> out_values;
    const int num_dims = a_shape->dim_size(0);

    OP_REQUIRES(ctx, num_dims > 0,
                errors::InvalidArgument("Invalid input_a shape. Received: ",
                                        a_shape->DebugString()));

    // The input and output sparse tensors are assumed to be ordered along
    // increasing dimension number.
    int64 i = 0, j = 0;
    T s;
    while (i < a_nnz && j < b_nnz) {
      switch (sparse::DimComparator::cmp(a_indices_mat, b_indices_mat, i, j,
                                         num_dims)) {
        case -1:
          entries_to_copy.emplace_back(true, i);
          out_values.push_back(a_values(i));
          ++i;
          break;
        case 0:
          s = a_values(i) + b_values(j);
          if (thresh <= std::abs(s)) {
            entries_to_copy.emplace_back(true, i);
            out_values.push_back(s);
          }
          ++i;
          ++j;
          break;
        case 1:
          entries_to_copy.emplace_back(false, j);
          out_values.push_back(b_values(j));
          ++j;
          break;
      }
    }

#define HANDLE_LEFTOVERS(A_OR_B, IDX, IS_A)     \
  while (IDX < A_OR_B##_nnz) {                  \
    entries_to_copy.emplace_back(IS_A, IDX);    \
    out_values.push_back(A_OR_B##_values(IDX)); \
    ++IDX;                                      \
  }

    // at most one of these calls appends new values
    HANDLE_LEFTOVERS(a, i, true);
    HANDLE_LEFTOVERS(b, j, false);
#undef HANDLE_LEFTOVERS

    // (2) allocate and fill output tensors
    const int64 sum_nnz = out_values.size();
    Tensor *out_indices_t, *out_values_t;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(0, TensorShape({sum_nnz, num_dims}),
                                        &out_indices_t));
    OP_REQUIRES_OK(
        ctx, ctx->allocate_output(1, TensorShape({sum_nnz}), &out_values_t));
    auto out_indices_mat = out_indices_t->matrix<int64>();
    auto out_values_flat = out_values_t->vec<T>();

    for (i = 0; i < sum_nnz; ++i) {
      const bool from_a = entries_to_copy[i].first;
      const int64 idx = entries_to_copy[i].second;
      out_indices_mat.chip<0>(i) =
          from_a ? a_indices_mat.chip<0>(idx) : b_indices_mat.chip<0>(idx);
    }
    if (sum_nnz > 0) {
      std::copy_n(out_values.begin(), sum_nnz, &out_values_flat(0));
    }
    ctx->set_output(2, *a_shape);
  }