Vulnerability-Lookup

CNVD-2021-36358

Vulnerability from cnvd - Published: 2021-05-18

Title

Google TensorFlow堆读取漏洞

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存在堆读取漏洞。攻击者可利用该漏洞触发从堆读取OOB。

Severity

中

Patch Name

Google TensorFlow堆读取漏洞的补丁

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存在堆读取漏洞。攻击者可利用该漏洞触发从堆读取OOB。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

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-29613

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-2021-36358",
  "openTime": "2021-05-18",
  "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\u5806\u8bfb\u53d6\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u89e6\u53d1\u4ece\u5806\u8bfb\u53d6OOB\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",
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-29613",
  "serverity": "\u4e2d",
  "submitTime": "2021-05-17",
  "title": "Google TensorFlow\u5806\u8bfb\u53d6\u6f0f\u6d1e"
}

CVE-2021-29613 (GCVE-0-2021-29613)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:20 – Updated: 2024-08-03 22:11

Title

Incomplete validation in `tf.raw_ops.CTCLoss`

Summary

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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

6.3 (Medium)


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

CWE

CWE-665 - Improper Initialization

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/1…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/4…	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

To clipboard

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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.