Vulnerability-Lookup

CNVD-2022-80684

Vulnerability from cnvd - Published: 2022-11-24

Title

Google TensorFlow缓冲区溢出漏洞（CNVD-2022-80684）

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，该安全漏洞导致 FractionalMax(AVG)Pool 具有非法的 pooling_ratio。使用Tensorflow的攻击者可以利用该漏洞。他们可以访问不受用户控制的堆内存，导致崩溃或远程代码执行。目前没有详细的漏洞细节提供。

Severity

高

Patch Name

Google TensorFlow缓冲区溢出漏洞（CNVD-2022-80684）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-xvwp-h6jv-7472

Reference

https://nvd.nist.gov/vuln/detail/CVE-2022-41900

Impacted products

Name
['Google TensorFlow <2.8.4', 'Google TensorFlow >=2.9.0，<2.9.3', 'Google TensorFlow 2.10.0']

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    "cve": {
      "cveNumber": "CVE-2022-41900",
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  "number": "CNVD-2022-80684",
  "openTime": "2022-11-24",
  "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\u5b89\u5168\u6f0f\u6d1e\u5bfc\u81f4 FractionalMax(AVG)Pool \u5177\u6709\u975e\u6cd5\u7684 pooling_ratio\u3002\u4f7f\u7528Tensorflow\u7684\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u3002\u4ed6\u4eec\u53ef\u4ee5\u8bbf\u95ee\u4e0d\u53d7\u7528\u6237\u63a7\u5236\u7684\u5806\u5185\u5b58\uff0c\u5bfc\u81f4\u5d29\u6e83\u6216\u8fdc\u7a0b\u4ee3\u7801\u6267\u884c\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",
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2022-41900",
  "serverity": "\u9ad8",
  "submitTime": "2022-11-23",
  "title": "Google TensorFlow\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2022-80684\uff09"
}

CVE-2022-41900 (GCVE-0-2022-41900)

Vulnerability from cvelistv5 – Published: 2022-11-18 00:00 – Updated: 2025-04-22 16:03

Title

FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow

Summary

TensorFlow is an open source platform for machine learning. The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution. We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1.

Severity

7.1 (High)


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

SSVC

Exploitation: poc Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-04-22 15:40 UTC

CWE

CWE-787 - Out-of-bounds Write
CWE-125 - Out-of-bounds Read

Assigner

GitHub_M mitre root

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…
https://github.com/tensorflow/tensorflow/commit/2…

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.10.0, < 2.10.1 Affected: >= 2.9.0, < 2.9.3 Affected: < 2.8.4

Show details on NVD website

JSON

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{
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          },
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            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "metrics": [
          {
            "other": {
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                "id": "CVE-2022-41900",
                "options": [
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                    "Exploitation": "poc"
                  },
                  {
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                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-22T15:40:35.291563Z",
                "version": "2.0.3"
              },
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        ],
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          "shortName": "CISA-ADP"
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    ],
    "cna": {
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          "versions": [
            {
              "status": "affected",
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            },
            {
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              "version": "\u003e= 2.9.0, \u003c 2.9.3"
            },
            {
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            }
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        }
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      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
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            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
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        {
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        "discovery": "UNKNOWN"
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      "title": "FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow"
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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-2022-80684

CVE-2022-41900 (GCVE-0-2022-41900)

Tags

Sightings

Nomenclature

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