Vulnerability-Lookup

CNVD-2021-92554

Vulnerability from cnvd - Published: 2021-11-30

Title

Google TensorFlow存在未明漏洞（CNVD-2021-92554）

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 存在安全漏洞，该漏洞源于 Grappler 优化器使用了未初始化的变量。如果 train_nodes 向量（从保存的优化模型中获得）不包含 Dequeue 节点，那么 dequeue_node 将被单元化。目前没有详细的漏洞细节提供。

Severity

低

Patch Name

Google TensorFlow存在未明漏洞（CNVD-2021-92554）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-7r94-xv9v-63jw

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-41225

Impacted products

Name
['Google TensorFlow >=2.5.0，<2.5.2', 'Google TensorFlow >=2.6.0，<2.6.1', 'Google TensorFlow >=2.4.0，<2.4.4']

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JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-41225"
    }
  },
  "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\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e Grappler \u4f18\u5316\u5668\u4f7f\u7528\u4e86\u672a\u521d\u59cb\u5316\u7684\u53d8\u91cf\u3002 \u5982\u679c train_nodes \u5411\u91cf\uff08\u4ece\u4fdd\u5b58\u7684\u4f18\u5316\u6a21\u578b\u4e2d\u83b7\u5f97\uff09\u4e0d\u5305\u542b Dequeue \u8282\u70b9\uff0c\u90a3\u4e48 dequeue_node \u5c06\u88ab\u5355\u5143\u5316\u3002 \u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-92554",
  "openTime": "2021-11-30",
  "patchDescription": "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\r\n\r\nGoogle TensorFlow \u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e Grappler \u4f18\u5316\u5668\u4f7f\u7528\u4e86\u672a\u521d\u59cb\u5316\u7684\u53d8\u91cf\u3002 \u5982\u679c train_nodes \u5411\u91cf\uff08\u4ece\u4fdd\u5b58\u7684\u4f18\u5316\u6a21\u578b\u4e2d\u83b7\u5f97\uff09\u4e0d\u5305\u542b Dequeue \u8282\u70b9\uff0c\u90a3\u4e48 dequeue_node \u5c06\u88ab\u5355\u5143\u5316\u3002 \u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "Google TensorFlow\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2021-92554\uff09\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Google TensorFlow \u003e=2.5.0\uff0c\u003c2.5.2",
      "Google TensorFlow \u003e=2.6.0\uff0c\u003c2.6.1",
      "Google TensorFlow \u003e=2.4.0\uff0c\u003c2.4.4"
    ]
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-41225",
  "serverity": "\u4f4e",
  "submitTime": "2021-11-09",
  "title": "Google TensorFlow\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2021-92554\uff09"
}

CVE-2021-41225 (GCVE-0-2021-41225)

Vulnerability from cvelistv5 – Published: 2021-11-05 22:30 – Updated: 2024-08-04 03:08

Title

A use of uninitialized value vulnerability in Tensorflow

Summary

TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. 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-908 - Use of Uninitialized Resource

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/6…	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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        }
      ],
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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.

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.

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CNVD-2021-92554

CVE-2021-41225 (GCVE-0-2021-41225)

Tags

Sightings

Nomenclature

Related by attack behaviour