Ecosyste.ms: Advisories
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Security Advisories: GSA_kwCzR0hTQS03eDR2LTlneGctOWh3as4AAyT1
TensorFlow has Segfault in Bincount with XLA
Impact
When running with XLA, tf.raw_ops.Bincount
segfaults when given a parameter weights
that is neither the same shape as parameter arr
nor a length-0 tensor.
import tensorflow as tf
func = tf.raw_ops.Bincount
para={'arr': 6, 'size': 804, 'weights': [52, 351]}
@tf.function(jit_compile=True)
def fuzz_jit():
y = func(**para)
return y
print(fuzz_jit())
Patches
We have patched the issue in GitHub commit 8ae76cf085f4be26295d2ecf2081e759e04b8acf.
The fix will be included in TensorFlow 2.12. We will also cherrypick this commit on TensorFlow 2.11.1.
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 r3pwnx of 360 AIVul Team
Permalink: https://github.com/advisories/GHSA-7x4v-9gxg-9hwjJSON: https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03eDR2LTlneGctOWh3as4AAyT1
Source: GitHub Advisory Database
Origin: Unspecified
Severity: High
Classification: General
Published: about 1 year ago
Updated: about 1 year ago
CVSS Score: 7.5
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Identifiers: GHSA-7x4v-9gxg-9hwj, CVE-2023-25675
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj
- https://github.com/tensorflow/tensorflow/commit/8ae76cf085f4be26295d2ecf2081e759e04b8acf
- https://nvd.nist.gov/vuln/detail/CVE-2023-25675
- https://github.com/advisories/GHSA-7x4v-9gxg-9hwj
Blast Radius: 36.5
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 146Dependent repositories: 11,499
Downloads: 354,712 last month
Affected Version Ranges: < 2.11.1
Fixed in: 2.11.1
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, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 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
All unaffected versions: 2.12.0
pypi:tensorflow-cpu
Dependent packages: 71Dependent repositories: 2,483
Downloads: 942,065 last month
Affected Version Ranges: < 2.11.1
Fixed in: 2.11.1
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, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 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
All unaffected versions: 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: 1,733Dependent repositories: 73,755
Downloads: 22,560,575 last month
Affected Version Ranges: < 2.11.1
Fixed in: 2.11.1
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, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 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
All unaffected versions: 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