CVE-2021-37677: 
Python vulnerability analysis and mitigation

TensorFlow, an end-to-end open source platform for machine learning, contained a vulnerability in versions prior to 2.6.0 (CVE-2021-37677). The shape inference code for tf.raw_ops.Dequantize had a vulnerability that could trigger a denial of service via a segfault when invalid arguments were provided (GitHub Advisory).

The vulnerability exists in the shape inference implementation which uses the 'axis' parameter to select between different values for minmax_rank, which is then used to retrieve tensor dimensions. The code incorrectly assumes that 'axis' can only be either -1 or a value greater than -1, without proper validation for other values. This missing validation could lead to a segmentation fault when invalid arguments are provided (GitHub Advisory).

When successfully exploited, this vulnerability could result in a denial of service condition through a segmentation fault in applications using the affected TensorFlow versions (GitHub Advisory).

The vulnerability has been patched in TensorFlow versions 2.3.4, 2.4.3, 2.5.1, and 2.6.0. Users are recommended to upgrade to these patched versions. The fix implements proper validation checks for the axis parameter (GitHub Advisory).