GHSA-9c8h-2mv3-49ww

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Source
https://github.com/advisories/GHSA-9c8h-2mv3-49ww
Import Source
https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/08/GHSA-9c8h-2mv3-49ww/GHSA-9c8h-2mv3-49ww.json
JSON Data
https://api.osv.dev/v1/vulns/GHSA-9c8h-2mv3-49ww
Aliases
Published
2021-08-25T14:41:29Z
Modified
2024-11-13T21:23:34.566939Z
Severity
  • 5.5 (Medium) CVSS_V3 - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H CVSS Calculator
  • 6.8 (Medium) CVSS_V4 - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N CVSS Calculator
Summary
Division by 0 in most convolution operators
Details

Impact

Most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash:

import tensorflow as tf

tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Conv2D(
  input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32),
  filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32),
  strides = [1, 1, 1, 1],
  padding = "SAME")

The shape inference implementation is missing several validations before doing divisions and modulo operations.

Patches

We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4.

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.

Database specific
{
    "nvd_published_at": "2021-08-12T22:15:00Z",
    "cwe_ids": [
        "CWE-369"
    ],
    "severity": "MODERATE",
    "github_reviewed": true,
    "github_reviewed_at": "2021-08-24T15:41:50Z"
}
References

Affected packages

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.3.4

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
2.1.4
2.2.0
2.2.1
2.2.2
2.2.3
2.3.0
2.3.1
2.3.2
2.3.3

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.3

Affected versions

2.*

2.4.0
2.4.1
2.4.2

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.5.0
Fixed
2.5.1

Affected versions

2.*

2.5.0

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.3.4

Affected versions

1.*

1.15.0

2.*

2.1.0
2.1.1
2.1.2
2.1.3
2.1.4
2.2.0
2.2.1
2.2.2
2.2.3
2.3.0
2.3.1
2.3.2
2.3.3

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.3

Affected versions

2.*

2.4.0
2.4.1
2.4.2

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.5.0
Fixed
2.5.1

Affected versions

2.*

2.5.0

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
2.3.4

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
2.1.4
2.2.0
2.2.1
2.2.2
2.2.3
2.3.0
2.3.1
2.3.2
2.3.3

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.4.0
Fixed
2.4.3

Affected versions

2.*

2.4.0
2.4.1
2.4.2

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.5.0
Fixed
2.5.1

Affected versions

2.*

2.5.0