tfsa-2020-016.md

TFSA-2020-016: Segfault due to invalid splits in RaggedCountSparseOutput

CVE Number

CVE-2020-15200

Impact

The RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Thus, the following code sets up conditions to cause a heap buffer overflow:

    auto per_batch_counts = BatchedMap<W>(num_batches);
    int batch_idx = 0;
    for (int idx = 0; idx < num_values; ++idx) {
      while (idx >= splits_values(batch_idx)) {
        batch_idx++;
      }
      const auto& value = values_values(idx);
      if (value >= 0 && (maxlength_ <= 0 || value < maxlength_)) {
        per_batch_counts[batch_idx - 1][value] = 1;
      }
    }

A BatchedMap is equivalent to a vector where each element is a hashmap. However, if the first element of splits_values is not 0, batch_idx will never be 1, hence there will be no hashmap at index 0 in per_batch_counts. Trying to access that in the user code results in a segmentation fault.

Vulnerable Versions

TensorFlow 2.3.0.

Patches

We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.

We recommend users to upgrade to TensorFlow 2.3.1.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been discovered through a variant analysis of a vulnerability reported by members of the Aivul Team from Qihoo 360.