Vulnerability-Lookup

CNVD-2022-13928

Vulnerability from cnvd - Published: 2022-02-23

Title

Google Tensorflow拒绝服务漏洞（CNVD-2022-13928）

Description

TensorFlow是一个端到端开源机器学习平台。它拥有一个全面而灵活的生态系统，其中包含各种工具、库和社区资源，可助力研究人员推动先进机器学习技术的发展，并使开发者能够轻松地构建和部署由机器学习提供支持的应用。 TensorFlow存在拒绝服务漏洞，该漏洞源于ThreadPoolHandle分配过多的内存，num_threads参数只被检查为非负数。攻击者可利用该漏洞导致拒绝服务。

Severity

中

Patch Name

Google Tensorflow拒绝服务漏洞（CNVD-2022-13928）的补丁

Patch Description

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq

Reference

https://nvd.nist.gov/vuln/detail/CVE-2022-21732

Impacted products

Name
['Google TensorFlow >=2.6.0，<=2.6.2', 'Google TensorFlow 2.7.0', 'Google TensorFlow <=2.5.2']

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  "serverity": "\u4e2d",
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}

CVE-2022-21732 (GCVE-0-2022-21732)

Vulnerability from cvelistv5 – Published: 2022-02-03 11:21 – Updated: 2025-02-12 15:58

Title

Memory exhaustion in Tensorflow

Summary

Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Severity

4.3 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-01-31 17:14 UTC

CWE

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/e…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/blob/510…	x_refsource_MISC

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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.

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.

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CNVD-2022-13928

CVE-2022-21732 (GCVE-0-2022-21732)

Tags

Sightings

Nomenclature

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