Vulnerability-Lookup

CNVD-2023-15782

Vulnerability from cnvd - Published: 2023-03-09

Title

Google TensorFlow拒绝服务漏洞（CNVD-2023-15782）

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，该漏洞源于TensorFlow中tf.reshape op的实现容易受到张量中元素数量溢出影响，出现CHECK-failure（断言失败），攻击者可利用该漏洞触发拒绝服务攻击。

Severity

高

Patch Name

Google TensorFlow拒绝服务漏洞（CNVD-2023-15782）的补丁

Patch Description

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-f4w6-h4f5-wx45

Reference

https://github.com/tensorflow/tensorflow/commit/61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555

Impacted products

Name
Google TensorFlow <2.10.0

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2022-35934",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2022-35934"
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  "number": "CNVD-2023-15782",
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  "title": "Google TensorFlow\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\uff08CNVD-2023-15782\uff09"
}

CVE-2022-35934 (GCVE-0-2022-35934)

Vulnerability from cvelistv5 – Published: 2022-09-16 19:30 – Updated: 2025-04-23 17:04

Title

`CHECK` failure in tf.reshape in Tensorflow

Summary

TensorFlow is an open source platform for machine learning. The implementation of tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This issue has been patched in GitHub commit 61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Severity

5.9 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-04-23 14:00 UTC

CWE

CWE-617 - Reachable Assertion

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: < 2.7.2 Affected: >= 2.8.0, < 2.8.1 Affected: >= 2.9.0, < 2.9.1

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.

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

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