GHSA-whr9-vfh2-7hm6 Memory corruption in `DrawBoundingBoxesV2`

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

Impact The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs: ```python import tensorflow as tf images = tf.fill([10, 96, 0, 1], 0.) boxes = tf.fill([10, 53, 0], 0.) colors = tf.fill([0, 1], 0.) tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors) ``` The implementation assumes that the last element of `boxes` input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption: ```cc const auto tboxes = boxes.tensor(); for (int64 bb = 0; bb (tboxes(b, bb, 0)) * (height - 1); const int64 max_box_row = static_cast(tboxes(b, bb, 2)) * (height - 1); const int64 min_box_col = static_cast(tboxes(b, bb, 1)) * (width - 1); const int64 max_box_col = static_cast(tboxes(b, bb, 3)) * (width - 1); ... } ``` If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. Patches We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517. 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. 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 Yakun Zhang and Ying Wang of Baidu X-Team.