TensorFlow is an end-to-end open source platform for machine learning. The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
| Package (Ecosystem) | Introduced | Fixed | Limit |
|---|---|---|---|
| tensorflow(PyPI) | 0 | 2.1.4 | N/A |
| tensorflow(PyPI) | 2.2.0 | 2.2.3 | N/A |
| tensorflow(PyPI) | 2.3.0 | 2.3.3 | N/A |
| tensorflow(PyPI) | 2.4.0 | 2.4.2 | N/A |
| tensorflow-cpu(PyPI) | 0 | 2.1.4 | N/A |
| tensorflow-cpu(PyPI) | 2.2.0 | 2.2.3 | N/A |
| tensorflow-cpu(PyPI) | 2.3.0 | 2.3.3 | N/A |
| tensorflow-cpu(PyPI) | 2.4.0 | 2.4.2 | N/A |
| tensorflow-gpu(PyPI) | 0 | 2.1.4 | N/A |
| tensorflow-gpu(PyPI) | 2.2.0 | 2.2.3 | N/A |
| tensorflow-gpu(PyPI) | 2.3.0 | 2.3.3 | N/A |
| tensorflow-gpu(PyPI) | 2.4.0 | 2.4.2 | N/A |
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