Vulnerability-Lookup

CNVD-2021-87032

Vulnerability from cnvd - Published: 2021-11-12

Title

Google TensorFlow资源管理错误漏洞

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 存在资源管理错误漏洞，该漏洞源于当两个tf.function修饰的Python函数相互递归时，`tf.function` API 背后的代码可能会陷入死锁。目前没有详细的漏洞细节提供。

Severity

中

Patch Name

Google TensorFlow资源管理错误漏洞的补丁

Patch Description

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-h67m-xg8f-fxcf

Reference

https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7

Impacted products

Name
Google TensorFlow

Show details on source website

JSON

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    "cve": {
      "cveNumber": "CVE-2021-41213"
    }
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  "description": "Google TensorFlow\u662f\u7f8e\u56fd\u8c37\u6b4c\uff08Google\uff09\u516c\u53f8\u7684\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\n\nGoogle TensorFlow \u5b58\u5728\u8d44\u6e90\u7ba1\u7406\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5f53\u4e24\u4e2atf.function\u4fee\u9970\u7684Python\u51fd\u6570\u76f8\u4e92\u9012\u5f52\u65f6\uff0c`tf.function` API \u80cc\u540e\u7684\u4ee3\u7801\u53ef\u80fd\u4f1a\u9677\u5165\u6b7b\u9501\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002",
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  "number": "CNVD-2021-87032",
  "openTime": "2021-11-12",
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  "serverity": "\u4e2d",
  "submitTime": "2021-11-09",
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}

CVE-2021-41213 (GCVE-0-2021-41213)

Vulnerability from cvelistv5 – Published: 2021-11-05 22:10 – Updated: 2024-08-04 03:08

Title

Deadlock in mutually recursive `tf.function` objects

Summary

TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Severity

5.5 (Medium)


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

CWE

CWE-667 - Improper Locking

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/a…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.6.0, < 2.6.1 Affected: >= 2.5.0, < 2.5.2 Affected: < 2.4.4

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JSON

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Sightings

Author	Source	Type	Date	Other

Nomenclature

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Confirmed: The vulnerability has been validated from an analyst's perspective.
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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.
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CNVD-2021-87032

CVE-2021-41213 (GCVE-0-2021-41213)

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