Vulnerability-Lookup

CNVD-2023-10602

Vulnerability from cnvd - Published: 2023-02-21

Title

Google TensorFlow AvgPoolOp拒绝服务漏洞

Description

Google TensorFlow是美国谷歌（Google）公司的一套用于机器学习的端到端开源平台。 Google TensorFlow存在拒绝服务漏洞，该漏洞源于AvgPoolOp函数接受一个参数ksize ，该参数必须为正但未检查。攻击者可利用漏洞通过负的ksize触发拒绝服务攻击。

Severity

高

Patch Name

Google TensorFlow AvgPoolOp拒绝服务漏洞的补丁

Patch Description

Google TensorFlow是美国谷歌（Google）公司的一套用于机器学习的端到端开源平台。 Google TensorFlow存在拒绝服务漏洞，该漏洞源于AvgPoolOp函数接受一个参数ksize ，该参数必须为正但未检查。攻击者可利用漏洞通过负的ksize触发拒绝服务攻击。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

Formal description

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5

Reference

https://github.com/tensorflow/tensorflow/commit/3a6ac52664c6c095aa2b114e742b0aa17fdce78f

Impacted products

Name
['Google TensorFlow >=2.8.0，<2.8.1', 'Google TensorFlow >=2.9.0，<2.9.1', 'Google TensorFlow 2.10-RC0', 'Google TensorFlow 2.10-RC1', 'Google TensorFlow 2.10-RC2', 'Google TensorFlow 2.10-RC3', 'Google TensorFlow >=2.7.0，<2.7.2']

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  "number": "CNVD-2023-10602",
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  "submitTime": "2022-09-20",
  "title": "Google TensorFlow AvgPoolOp\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e"
}

CVE-2022-35941 (GCVE-0-2022-35941)

Vulnerability from cvelistv5 – Published: 2022-09-16 19:45 – Updated: 2025-04-23 17:04

Title

`CHECK` failure in `AvgPoolOp` in Tensorflow

Summary

TensorFlow is an open source platform for machine learning. The `AvgPoolOp` function takes an argument `ksize` that must be positive but is not checked. A negative `ksize` can trigger a `CHECK` failure and crash the program. We have patched the issue in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds to this issue.

Severity

5.9 (Medium)


                        
                          CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-04-23 14:00 UTC

CWE

CWE-617 - Reachable Assertion

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/3…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/blob/8d7…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.7.2 Affected: >= 2.8.0, < 2.8.1 Affected: >= 2.9.0, < 2.9.1

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JSON

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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.