CVE-2020-15266: 
Python vulnerability analysis and mitigation

CVE-2020-15266 affects TensorFlow versions before 2.4.0, specifically related to the tf.image.crop_and_resize function. The vulnerability was discovered on June 25, 2020, and involves a floating-point overflow issue that could lead to undefined behavior and segmentation faults. The issue occurs when the 'boxes' argument contains very large values, causing the CPU kernel implementation to receive it as a C++ nan floating point value (GitHub Advisory).

The vulnerability manifests when the boxes argument of tf.image.crop_and_resize contains very large values (e.g., 1.0e+40) or non-finite values (inf or nan). When such values are processed, the CPU kernel implementation incorrectly handles them as C++ nan floating point values, leading to undefined behavior and subsequent segmentation faults (GitHub Issue). The issue was assigned a Low severity rating, indicating limited impact on system security.

When exploited, this vulnerability results in a segmentation fault, which can cause the application to crash. This primarily affects applications using TensorFlow's image processing capabilities, specifically the tf.image.crop_and_resize function with extreme input values (GitHub Advisory).

The vulnerability can be triggered by providing very large values (such as 1.0e+40) to the 'boxes' argument of the tf.image.crop_and_resize function. A proof-of-concept exploit involves calling the function with zeros for the image and a large value for the boxes parameter (GitHub Issue).

The issue has been patched in TensorFlow version 2.4.0. The fix includes additional validation to ensure that box values are finite before processing. Users are recommended to upgrade to TensorFlow 2.4.0 or later versions. The patch was implemented in commit eccb7ec454e6617738554a255d77f08e60ee0808 (GitHub Advisory).