Vulnerability-Lookup

CNVD-2020-54783

Vulnerability from cnvd - Published: 2020-09-30

Title

Tensorflow数据验证漏洞

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。 Tensorflow 2.3.0版本中存在安全漏洞，该漏洞源于SparseCountSparseOutput和RaggedCountSparseOutput实现无法验证weights张量与数据的形状相同。攻击者可利用该漏洞执行本地恶意应用程序绕过用户交互要求，以访问其他权限。

Severity

高

Patch Name

Tensorflow数据验证漏洞的补丁

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/releases/tag/v2.3.1

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-15196

Impacted products

Name
Google TensorFlow 2.3.0

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-15196",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-15196"
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  "number": "CNVD-2020-54783",
  "openTime": "2020-09-30",
  "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\nTensorflow  2.3.0\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8eSparseCountSparseOutput\u548cRaggedCountSparseOutput\u5b9e\u73b0\u65e0\u6cd5\u9a8c\u8bc1weights\u5f20\u91cf\u4e0e\u6570\u636e\u7684\u5f62\u72b6\u76f8\u540c\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u672c\u5730\u6076\u610f\u5e94\u7528\u7a0b\u5e8f\u7ed5\u8fc7\u7528\u6237\u4ea4\u4e92\u8981\u6c42\uff0c\u4ee5\u8bbf\u95ee\u5176\u4ed6\u6743\u9650\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-2020-15196",
  "serverity": "\u9ad8",
  "submitTime": "2020-09-28",
  "title": "Tensorflow\u6570\u636e\u9a8c\u8bc1\u6f0f\u6d1e"
}

CVE-2020-15196 (GCVE-0-2020-15196)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:40 – Updated: 2024-08-04 13:08

Title

Heap buffer overflow in Tensorflow

Summary

In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Severity

8.5 (High)


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

CWE

CWE-119 - {"CWE-119":"Improper Restriction of Operations within the Bounds of a Memory Buffer"}
CWE-122 - {"CWE-122":"Heap-based Buffer Overflow"}

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/3…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: = 2.3.0

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

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.