Vulnerability-Lookup

CNVD-2019-42740

Vulnerability from cnvd - Published: 2019-11-28

Title

Sony Neural Network Libraries输入验证错误漏洞

Description

Neural Network Libraries是一个深度学习框架，旨在用于研究、开发和生产。 Sony Neural Network Libraries (nnabla) 1.0.14及更早版本中的libnnabla.a中的nbla/logger.cpp存在输入验证错误漏洞。该漏洞源于该软件依赖可能不可信的HOME环境变量。目前没有详细的漏洞细节提供。

Severity

高

Patch Name

Sony Neural Network Libraries输入验证错误漏洞的补丁

Patch Description

Formal description

厂商已发布漏洞修复程序，请及时关注更新： https://github.com/sony/nnabla/commit/e87347648ab7210529a0e60f0849680de8e9b63a

Reference

https://nvd.nist.gov/vuln/detail/CVE-2019-10844

Impacted products

Name
Sony Sony Neural Network Libraries <=1.0.14

Show details on source website

JSON

To clipboard

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  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-42740",
  "openTime": "2019-11-28",
  "patchDescription": "Neural Network Libraries\u662f\u4e00\u4e2a\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u65e8\u5728\u7528\u4e8e\u7814\u7a76\u3001\u5f00\u53d1\u548c\u751f\u4ea7\u3002\r\n\r\nSony Neural Network Libraries (nnabla) 1.0.14\u53ca\u66f4\u65e9\u7248\u672c\u4e2d\u7684libnnabla.a\u4e2d\u7684nbla/logger.cpp\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8be5\u8f6f\u4ef6\u4f9d\u8d56\u53ef\u80fd\u4e0d\u53ef\u4fe1\u7684HOME\u73af\u5883\u53d8\u91cf\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": "Sony Neural Network Libraries\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Sony Sony Neural Network Libraries \u003c=1.0.14"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-10844",
  "serverity": "\u9ad8",
  "submitTime": "2019-04-04",
  "title": "Sony Neural Network Libraries\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e"
}

CVE-2019-10844 (GCVE-0-2019-10844)

Vulnerability from cvelistv5 – Published: 2019-04-04 04:19 – Updated: 2024-08-04 22:32

Summary

nbla/logger.cpp in libnnabla.a in Sony Neural Network Libraries (aka nnabla) through v1.0.14 relies on the HOME environment variable, which might be untrusted.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

1 reference

URL	Tags
https://github.com/sony/nnabla/issues/209	x_refsource_MISC

Show details on NVD website

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.