GHSA-wf5p-c75w-w3wh

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Source
https://github.com/advisories/GHSA-wf5p-c75w-w3wh
Import Source
https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/08/GHSA-wf5p-c75w-w3wh/GHSA-wf5p-c75w-w3wh.json
JSON Data
https://api.osv.dev/v1/vulns/GHSA-wf5p-c75w-w3wh
Aliases
Published
2021-08-25T14:39:36Z
Modified
2024-11-13T21:53:16.340660Z
Severity
  • 7.8 (High) CVSS_V3 - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H CVSS Calculator
  • 8.5 (High) CVSS_V4 - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N CVSS Calculator
Summary
Null pointer dereference in TFLite MLIR optimizations
Details

Impact

An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service:

This is caused by the MLIR optimization of L2NormalizeReduceAxis operator. The implementation unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements:

bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) {
  if (sq_op.getType().cast<ShapedType>().getRank() - 1 ==
          *axis.getValues<int>().begin() ||
      *axis.getValues<int>().begin() == -1) {
      // ...
  }
  // ...
}

Patches

We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.

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.

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