Vulnerability-Lookup

CNVD-2021-63060

Vulnerability from cnvd - Published: 2021-08-19

Title

Google TensorFlow拒绝服务漏洞（CNVD-2021-63060）

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，本地攻击者可利用此漏洞造成拒绝服务。

Severity

低

Patch Name

Google TensorFlow拒绝服务漏洞（CNVD-2021-63060）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，本地攻击者可利用此漏洞造成拒绝服务。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

目前厂商已经发布了升级补丁以修复这个安全问题，请到厂商的主页下载： https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw

Reference

https://exchange.xforce.ibmcloud.com/vulnerabilities/207468

Impacted products

Name
Google TensorFlow <2.6.0

Show details on source website

JSON

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    "cve": {
      "cveNumber": "CVE-2021-37682",
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  "number": "CNVD-2021-63060",
  "openTime": "2021-08-19",
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  "products": {
    "product": "Google TensorFlow \u003c2.6.0"
  },
  "referenceLink": "https://exchange.xforce.ibmcloud.com/vulnerabilities/207468",
  "serverity": "\u4f4e",
  "submitTime": "2021-08-16",
  "title": "Google TensorFlow\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\uff08CNVD-2021-63060\uff09"
}

CVE-2021-37682 (GCVE-0-2021-37682)

Vulnerability from cvelistv5 – Published: 2021-08-12 22:45 – Updated: 2024-08-04 01:23

Title

Use of unitialized value in TensorFlow Lite

Summary

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Severity

4.4 (Medium)


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

CWE

CWE-908 - Use of Uninitialized Resource

Assigner

GitHub_M mitre root

References

4 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/4…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/5…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/8…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.5.0, < 2.5.1 Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4

Show details on NVD website

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.

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-2021-63060

CVE-2021-37682 (GCVE-0-2021-37682)

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