CVE-2020-15197 (Python, published 2020-09-25T19:15Z)
Description: value: In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Severity: LOW (CVSS v3 base score: 6.3)
Weakness: CWE-20 \u2014 Improper Input Validation
The product receives input or data, but it does
        not validate or incorrectly validates that the input has the
        properties that are required to process the data safely and
        correctly.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/core/kernels/count_ops.cc
tensorflow/python/ops/bincount_ops_test.py
Commit message: Fix multiple vulnerabilities in `tf.raw_ops.*CountSparseOutput`.

Also add tests for these API points, both for the happy paths and for the vulnerable ones.

PiperOrigin-RevId: 332563222
Change-Id: Ib3b52116a83a134c2e742a7c66e5e956db8fba05


Vulnerable code:

Fixed code:
    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(indices.shape()),
                errors::InvalidArgument(
                    "Input indices must be a 2-dimensional tensor. Got: ",
                    indices.shape().DebugString()));

    if (use_weights) {
      OP_REQUIRES(
          context, weights.shape() == values.shape(),
          errors::InvalidArgument(
              "Weights and values must have the same shape. Weight shape: ",
              weights.shape().DebugString(),
              "; values shape: ", values.shape().DebugString()));
    }

    OP_REQUIRES(context, num_values == indices.shape().dim_size(0),
                errors::InvalidArgument(
                    "Number of values must match first dimension of indices.",
                    "Got ", num_values,
                    " values, indices shape: ", indices.shape().DebugString()));

    if (use_weights) {
      OP_REQUIRES(
          context, weights.shape() == values.shape(),
          errors::InvalidArgument(
              "Weights and values must have the same shape. Weight shape: ",
              weights.shape().DebugString(),
              "; values shape: ", values.shape().DebugString()));
    }

    OP_REQUIRES(
        context, num_batches > 0,
        errors::InvalidArgument(
            "Must provide at least 2 elements for the splits argument"));
    OP_REQUIRES(context, splits_values(0) == 0,
                errors::InvalidArgument("Splits must start with 0, not with ",
                                        splits_values(0)));
    OP_REQUIRES(context, splits_values(num_batches) == num_values,
                errors::InvalidArgument(
                    "Splits must end with the number of values, got ",
                    splits_values(num_batches), " instead of ", num_values));

from tensorflow.python.framework import test_util
from tensorflow.python.ops import gen_count_ops
@test_util.run_all_in_graph_and_eager_modes
@test_util.disable_tfrt
class RawOpsTest(test.TestCase, parameterized.TestCase):

  def testSparseCountSparseOutputBadIndicesShape(self):
    indices = [[[0], [0]], [[0], [1]], [[1], [0]], [[1], [2]]]
    values = [1, 1, 1, 10]
    weights = [1, 2, 4, 6]
    dense_shape = [2, 3]
    with self.assertRaisesRegex(errors.InvalidArgumentError,
                                "Input indices must be a 2-dimensional tensor"):
      self.evaluate(
          gen_count_ops.SparseCountSparseOutput(
              indices=indices,
              values=values,
              dense_shape=dense_shape,
              weights=weights,
              binary_output=False))

  def testSparseCountSparseOutputBadWeightsShape(self):
    indices = [[0, 0], [0, 1], [1, 0], [1, 2]]
    values = [1, 1, 1, 10]
    weights = [1, 2, 4]
    dense_shape = [2, 3]
    with self.assertRaisesRegex(errors.InvalidArgumentError,
                                "Weights and values must have the same shape"):
      self.evaluate(
          gen_count_ops.SparseCountSparseOutput(
              indices=indices,
              values=values,
              dense_shape=dense_shape,
              weights=weights,
              binary_output=False))

  def testSparseCountSparseOutputBadNumberOfValues(self):
    indices = [[0, 0], [0, 1], [1, 0]]
    values = [1, 1, 1, 10]
    weights = [1, 2, 4, 6]
    dense_shape = [2, 3]
    with self.assertRaisesRegex(
        errors.InvalidArgumentError,
        "Number of values must match first dimension of indices"):
      self.evaluate(
          gen_count_ops.SparseCountSparseOutput(
              indices=indices,
              values=values,
              dense_shape=dense_shape,
              weights=weights,
              binary_output=False))

  def testRaggedCountSparseOutput(self):
    splits = [0, 4, 7]
    values = [1, 1, 2, 1, 2, 10, 5]
    weights = [1, 2, 3, 4, 5, 6, 7]
    output_indices, output_values, output_shape = self.evaluate(
        gen_count_ops.RaggedCountSparseOutput(
            splits=splits, values=values, weights=weights, binary_output=False))
    self.assertAllEqual([[0, 1], [0, 2], [1, 2], [1, 5], [1, 10]],
                        output_indices)
    self.assertAllEqual([7, 3, 5, 7, 6], output_values)
    self.assertAllEqual([2, 11], output_shape)

  def testRaggedCountSparseOutputBadWeightsShape(self):
    splits = [0, 4, 7]
    values = [1, 1, 2, 1, 2, 10, 5]
    weights = [1, 2, 3, 4, 5, 6]
    with self.assertRaisesRegex(errors.InvalidArgumentError,
                                "Weights and values must have the same shape"):
      self.evaluate(
          gen_count_ops.RaggedCountSparseOutput(
              splits=splits,
              values=values,
              weights=weights,
              binary_output=False))

  def testRaggedCountSparseOutputEmptySplits(self):
    splits = []
    values = [1, 1, 2, 1, 2, 10, 5]
    weights = [1, 2, 3, 4, 5, 6, 7]
    with self.assertRaisesRegex(
        errors.InvalidArgumentError,
        "Must provide at least 2 elements for the splits argument"):
      self.evaluate(
          gen_count_ops.RaggedCountSparseOutput(
              splits=splits,
              values=values,
              weights=weights,
              binary_output=False))

  def testRaggedCountSparseOutputBadSplitsStart(self):
    splits = [1, 7]
    values = [1, 1, 2, 1, 2, 10, 5]
    weights = [1, 2, 3, 4, 5, 6, 7]
    with self.assertRaisesRegex(errors.InvalidArgumentError,
                                "Splits must start with 0"):
      self.evaluate(
          gen_count_ops.RaggedCountSparseOutput(
              splits=splits,
              values=values,
              weights=weights,
              binary_output=False))

  def testRaggedCountSparseOutputBadSplitsEnd(self):
    splits = [0, 5]
    values = [1, 1, 2, 1, 2, 10, 5]
    weights = [1, 2, 3, 4, 5, 6, 7]
    with self.assertRaisesRegex(errors.InvalidArgumentError,
                                "Splits must end with the number of values"):
      self.evaluate(
          gen_count_ops.RaggedCountSparseOutput(
              splits=splits,
              values=values,
              weights=weights,
              binary_output=False))