TensorFlow has Floating Point Exception in AvgPoolGrad with XLA

### Impact If the stride and window size are not positive for `tf.raw_ops.AvgPoolGrad`, it can give an FPE. ```python import tensorflow as tf import numpy as np @tf.function(jit_compile=True) def test(): y = tf.raw_ops.AvgPoolGrad(orig_input_shape=[1,0,0,0], grad=[[[[0.39117979]]]], ksize=[1,0,0,0], strides=[1,0,0,0], padding="SAME", data_format="NCHW") return y print(test()) ``` ### Patches We have patched the issue in GitHub commit [1295ae4dbb52fe06b19733b0257e2340d7b63b8d](https://github.com/tensorflow/tensorflow/commit/1295ae4dbb52fe06b19733b0257e2340d7b63b8d). The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by r3pwnx of 360 AIVul Team

CVSS 3.1

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Severity from

GitHub (reviewed advisory)

Weakness

CWE-697

Also known as

BIT-tensorflow-2023-25669, CVE-2023-25669, PYSEC-2026-1964, PYSEC-2026-3236, PYSEC-2026-3366

• github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p
• nvd.nist.gov/vuln/detail/CVE-2023-25669
• github.com/tensorflow/tensorflow/commit/1295ae4dbb52fe06b19733b0257e2340d7b63b8d
• github.com/tensorflow/tensorflow