CVE-2020-15213 (C++, published 2020-09-25T19:15Z)
Description: value: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Severity: MEDIUM (CVSS v3 base score: 4)
Weakness: CWE-770 \u2014 Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any restrictions on the size or number of resources that can be allocated, in violation of the intended security policy for that actor.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/lite/kernels/segment_sum.cc
tensorflow/lite/kernels/segment_sum_test.cc
Commit message: [tflite] Validate segment ids for segment_sum.

Segment identifiers in segment_sum should be in a 1-D tensor of same size as the first dimension of the input. The values of the tensor should be integers from {0, 1, 2, ... k-1}, where k is the first dimension of the input. The segment identifiers must not contain jumps and must be increasing.

See https://www.tensorflow.org/api_docs/python/tf/math#Segmentation as the source for these constraints.

PiperOrigin-RevId: 332510942
Change-Id: I898beaba00642c918bcd4b4d4ce893ebb190d869


Vulnerable code:
  int max_index = -1;
  if (segment_id_size > 0) {
    max_index = segment_ids->data.i32[segment_id_size - 1];

Fixed code:
  // Segment ids should be of same cardinality as first input dimension and they
  // should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
  TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
  int previous_segment_id = -1;
  for (int i = 0; i < segment_id_size; i++) {
    const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
    if (i == 0) {
      TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
    } else {
      int delta = current_segment_id - previous_segment_id;
      TF_LITE_ENSURE(context, delta == 0 || delta == 1);
    }
    previous_segment_id = current_segment_id;

  const int max_index = previous_segment_id;

TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
                                   {TensorType_INT32, {3}});
  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
}

TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
                                   {TensorType_INT32, {3}});
  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
}

TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
                                   {TensorType_INT32, {3}});
  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
  model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
}

TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
                                   {TensorType_INT32, {2}});
  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
}