exploitDog

GHSA-c45w-2wxr-pp53

Опубликовано: 21 мая 2021

Источник: github

Github: Прошло ревью

CVSS4: 2

CVSS3: 2.5

Описание

Heap OOB read in tf.raw_ops.Dequantize

Impact

Due to lack of validation in tf.raw_ops.Dequantize, an attacker can trigger a read from outside of bounds of heap allocated data:

import tensorflow as tf input_tensor=tf.constant( [75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10], shape=[5, 10], dtype=tf.int32) input_tensor=tf.cast(input_tensor, dtype=tf.quint8) min_range = tf.constant([-10], shape=[1], dtype=tf.float32) max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32) tf.raw_ops.Dequantize( input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED', narrow_range=True, axis=0, dtype=tf.dtypes.float32)

The implementation accesses the min_range and max_range tensors in parallel but fails to check that they have the same shape:

if (num_slices == 1) { const float min_range = input_min_tensor.flat<float>()(0); const float max_range = input_max_tensor.flat<float>()(0); DequantizeTensor(ctx, input, min_range, max_range, &float_output); } else { ... auto min_ranges = input_min_tensor.vec<float>(); auto max_ranges = input_max_tensor.vec<float>(); for (int i = 0; i < num_slices; ++i) { DequantizeSlice(ctx->eigen_device<Device>(), ctx, input_tensor.template chip<1>(i), min_ranges(i), max_ranges(i), output_tensor.template chip<1>(i)); ... } }

Patches

We have patched the issue in GitHub commit 5899741d0421391ca878da47907b1452f06aaf1b.

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 Yakun Zhang and Ying Wang of Baidu X-Team.

Ссылки

Пакеты

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow-cpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

< 2.1.4

2.1.4

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.2.0, < 2.2.3

2.2.3

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.3.0, < 2.3.3

2.3.3

Наименование

tensorflow-gpu

pip

Затронутые версииВерсия исправления

>= 2.4.0, < 2.4.2

2.4.2

EPSS

Процентиль: 3%

0.00017

Низкий

2 Low

CVSS4

2.5 Low

CVSS3

CVE ID

Дефекты

CWE-125

Связанные уязвимости

CVE-2021-29582

CVSS3: 2.5

nvd

больше 4 лет назад

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. 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.

CVE-2021-29582

CVSS3: 2.5

debian

больше 4 лет назад

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

EPSS

Процентиль: 3%

0.00017

Низкий

2 Low

CVSS4

2.5 Low

CVSS3

CVE ID

Дефекты

CWE-125