GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CNVD-2021-09323

Vulnerability from cnvd - Published: 2021-02-05
VLAI
Title
Nim输入验证错误漏洞
Description
Nim是Nim社区的一种静态类型的编程语言。 Nim 1.2.6之前版本存在输入验证错误漏洞,该漏洞源于标准库asyncftpclient未能检查消息是否包含换行符。目前没有详细的漏洞细节提供。
Severity
Patch Name
Nim输入验证错误漏洞的补丁
Patch Description
Nim是Nim社区的一种静态类型的编程语言。 Nim 1.2.6之前版本存在输入验证错误漏洞,该漏洞源于标准库asyncftpclient未能检查消息是否包含换行符。目前没有详细的漏洞细节提供。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/nim-lang/Nim/compare/v1.2.4...v1.2.6

Reference
https://github.com/nim-lang/Nim/blob/dc5a40f3f39c6ea672e6dc6aca7f8118a69dda99/lib/pure/asyncftpclient.nim#L145
Impacted products
Name
Nim Nim <1.2.6
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-15690",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-15690"
    }
  },
  "description": "Nim\u662fNim\u793e\u533a\u7684\u4e00\u79cd\u9759\u6001\u7c7b\u578b\u7684\u7f16\u7a0b\u8bed\u8a00\u3002\n\nNim 1.2.6\u4e4b\u524d\u7248\u672c\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u6807\u51c6\u5e93asyncftpclient\u672a\u80fd\u68c0\u67e5\u6d88\u606f\u662f\u5426\u5305\u542b\u6362\u884c\u7b26\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a\r\nhttps://github.com/nim-lang/Nim/compare/v1.2.4...v1.2.6",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-09323",
  "openTime": "2021-02-05",
  "patchDescription": "Nim\u662fNim\u793e\u533a\u7684\u4e00\u79cd\u9759\u6001\u7c7b\u578b\u7684\u7f16\u7a0b\u8bed\u8a00\u3002\r\n\r\nNim 1.2.6\u4e4b\u524d\u7248\u672c\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u6807\u51c6\u5e93asyncftpclient\u672a\u80fd\u68c0\u67e5\u6d88\u606f\u662f\u5426\u5305\u542b\u6362\u884c\u7b26\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": "Nim\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Nim Nim \u003c1.2.6"
  },
  "referenceLink": "https://github.com/nim-lang/Nim/blob/dc5a40f3f39c6ea672e6dc6aca7f8118a69dda99/lib/pure/asyncftpclient.nim#L145",
  "serverity": "\u9ad8",
  "submitTime": "2021-02-03",
  "title": "Nim\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…