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githubGitHub Advisory DatabaseGHSA-QFPC-5PJR-MH26
HistoryAug 25, 2021 - 2:41 p.m.

Missing validation in shape inference for `Dequantize`

2021-08-2514:41:23
CWE-20
CWE-1284
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

12.7%

Impact

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.

Patches

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.

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 Yakun Zhang of Baidu Security.

Affected configurations

Vulners
Node
tensorflowgpuMatch2.5.0
OR
tensorflowgpuRange<2.4.3
OR
tensorflowgpuRange<2.3.4
OR
tensorflowcpuMatch2.5.0
OR
tensorflowcpuRange<2.4.3
OR
tensorflowcpuRange<2.3.4
OR
github_advisory_databasetensorflowMatch2.5.0
OR
github_advisory_databasetensorflowRange<2.4.3
OR
github_advisory_databasetensorflowRange<2.3.4

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

12.7%