GHSA-4278-2v5v-65r4 Heap buffer overflow in `RaggedBinCount`

• PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

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.raw_ops.RaggedBincount(splits=[0], values=[1,1,1,1,1], size=5, weights=[1,2,3,4], binary_output=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 = splits(batch_idx)) { batch_idx++; } ... } ``` Before the `for` loop, `batch_idx` 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 `batch_idx` 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.