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Security Advisories: GSA_kwCzR0hTQS03N2dwLTNoNHItNjQyOM0ouA

Out of bounds read and write in Tensorflow

Impact

There is a typo in TensorFlow's SpecializeType which results in heap OOB read/write:

for (int i = 0; i < op_def.output_arg_size(); i++) {
  // ...
  for (int j = 0; j < t->args_size(); j++) {
    auto* arg = t->mutable_args(i);
    // ...
  }
} 

Due to a typo, arg is initialized to the ith mutable argument in a loop where the loop index is j. Hence it is possible to assign to arg from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data.

Patches

We have patched the issue in GitHub commit 0657c83d08845cc434175934c642299de2c0f042.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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.

Permalink: https://github.com/advisories/GHSA-77gp-3h4r-6428
JSON: https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03N2dwLTNoNHItNjQyOM0ouA
Source: GitHub Advisory Database
Origin: Unspecified
Severity: High
Classification: General
Published: about 2 years ago
Updated: about 1 year ago


CVSS Score: 8.8
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Identifiers: GHSA-77gp-3h4r-6428, CVE-2022-23574
References: Repository: https://github.com/tensorflow/tensorflow
Blast Radius: 42.8

Affected Packages

pypi:tensorflow-gpu
Dependent packages: 146
Dependent repositories: 11,499
Downloads: 354,712 last month
Affected Version Ranges: = 2.7.0, >= 2.6.0, < 2.6.3, < 2.5.3
Fixed in: 2.7.1, 2.6.3, 2.5.3
All affected versions: 0.12.0, 0.12.1, 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, 2.0.0, 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, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.6.0, 2.6.1, 2.6.2, 2.7.0
All unaffected versions: 2.5.3, 2.6.3, 2.6.4, 2.6.5, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 71
Dependent repositories: 2,483
Downloads: 942,065 last month
Affected Version Ranges: = 2.7.0, >= 2.6.0, < 2.6.3, < 2.5.3
Fixed in: 2.7.1, 2.6.3, 2.5.3
All affected versions: 1.15.0, 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, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.6.0, 2.6.1, 2.6.2, 2.7.0
All unaffected versions: 2.5.3, 2.6.3, 2.6.4, 2.6.5, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1
pypi:tensorflow
Dependent packages: 1,733
Dependent repositories: 73,755
Downloads: 22,560,575 last month
Affected Version Ranges: = 2.7.0, >= 2.6.0, < 2.6.3, < 2.5.3
Fixed in: 2.7.1, 2.6.3, 2.5.3
All affected versions: 0.12.0, 0.12.1, 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, 2.0.0, 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, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.6.0, 2.6.1, 2.6.2, 2.7.0
All unaffected versions: 2.5.3, 2.6.3, 2.6.4, 2.6.5, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1