Heap buffer overflow in `RaggedBinCount`

Summary

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). 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. 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.

Timeline

1. May 14, 2021, 6:55 PM UTCCVE published
2. Aug 3, 2024, 10:11 PM UTCRecord last updated

Affected versions

• tensorflow tensorflow

  From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2

• tensorflowPyPI

  From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2

• tensorflowPyPI

  From 2.3.0 · fixed in 2.3.3

• tensorflowPyPI

  From 2.4.0 · fixed in 2.4.2

• tensorflow-cpuPyPI

  From 2.3.0 · fixed in 2.3.3

• tensorflow-cpuPyPI

  From 2.4.0 · fixed in 2.4.2

• tensorflow-gpuPyPI

  From 2.3.0 · fixed in 2.3.3

• tensorflow-gpuPyPI

  From 2.4.0 · fixed in 2.4.2

• tensorflow-cpuPyPI

  From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2

• tensorflow-gpuPyPI

  From 2.3.0 · fixed in 2.3.3; From 2.4.0 · fixed in 2.4.2

References 2