CVE-2021-29512: Heap buffer overflow in `RaggedBinCount`

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

python import tensorflow as tf tf.rawops.RaggedBincount(splits=[0], values=[1,1,1,1,1], size=5, weights=[1,2,3,4], binaryoutput=False)

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

cc for (int idx = 0; idx < numvalues; ++idx) { while (idx >= splits(batchidx)) { batchidx++; } ... }

Before the for loop, batchidx is set to 0. The user controls the splits array, making it contain only one element, 0. Thus, the code in the while loop would increment batchidx and then try to read splits(1), which is outside of bounds.

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.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.