GHSA-mxjj-953w-2c2v Data corruption in tensorflow-lite

• PyPI tensorflow: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-cpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-cpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-cpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-gpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-gpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-gpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1

Impact When determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442 Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. Patches We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 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 reported by members of the Aivul Team from Qihoo 360.