TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
History

No history.

cve-icon MITRE

Status: PUBLISHED

Assigner: GitHub_M

Published: 2021-08-12T21:00:19

Updated: 2024-08-04T01:23:01.436Z

Reserved: 2021-07-29T00:00:00

Link: CVE-2021-37651

cve-icon Vulnrichment

No data.

cve-icon NVD

Status : Analyzed

Published: 2021-08-12T21:15:08.170

Modified: 2021-08-18T14:46:59.387

Link: CVE-2021-37651

cve-icon Redhat

No data.