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CNVD-2021-36332

Vulnerability from cnvd - Published: 2021-05-19

Title

Google TensorFlow拒绝服务漏洞（CNVD-2021-36332）

Description

Google TensorFlow是一个端到端开源机器学习平台。 Google TensorFlow中的pooling存在安全漏洞。目前没有详细的漏洞细节提供。

Severity

低

Patch Name

Google TensorFlow拒绝服务漏洞（CNVD-2021-36332）的补丁

Patch Description

Google TensorFlow是一个端到端开源机器学习平台。 Google TensorFlow中的pooling存在安全漏洞。目前没有详细的漏洞细节提供。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

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-26j7-6w8w-7922

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-29586

Impacted products

Name
['Google TensorFlow 2.4.2', 'Google TensorFlow 2.3.3', 'Google TensorFlow 2.2.3', 'Google TensorFlow 2.1.4']

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JSON

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  "number": "CNVD-2021-36332",
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  "serverity": "\u4f4e",
  "submitTime": "2021-05-17",
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}

CVE-2021-29586 (GCVE-0-2021-29586)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:35 – Updated: 2024-08-03 22:11

Title

Division by zero in optimized pooling implementations in TFLite

Summary

TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Severity

2.5 (Low)


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

CWE

CWE-369 - Divide By Zero

Assigner

GitHub_M mitre root

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/5…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.1.4 Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2

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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.