exploitDog

GHSA-qfpc-5pjr-mh26

Опубликовано: 25 авг. 2021

Источник: github

Github: Прошло ревью

CVSS4: 6.8

CVSS3: 5.5

Описание

Missing validation in shape inference for Dequantize

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.

Ссылки

Пакеты

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

< 2.3.4

2.3.4

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.3

2.4.3

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

= 2.5.0

2.5.1

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

< 2.3.4

2.3.4

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.3

2.4.3

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

= 2.5.0

2.5.1

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

< 2.3.4

2.3.4

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.3

2.4.3

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

= 2.5.0

2.5.1

EPSS

Процентиль: 1%

0.00009

Низкий

6.8 Medium

CVSS4

5.5 Medium

CVSS3

CVE ID

Дефекты

CWE-1284

CWE-20

Связанные уязвимости

CVE-2021-37677

CVSS3: 5.5

nvd

больше 4 лет назад

TensorFlow is an end-to-end open source platform for machine learning. In affected versions 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. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) 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. 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.

CVE-2021-37677

CVSS3: 5.5

debian

больше 4 лет назад

TensorFlow is an end-to-end open source platform for machine learning. ...

openSUSE-SU-2022:10014-1

suse-cvrf

больше 3 лет назад

Security update for tensorflow2

EPSS

Процентиль: 1%

0.00009

Низкий

6.8 Medium

CVSS4

5.5 Medium

CVSS3

CVE ID

Дефекты

CWE-1284

CWE-20