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CNVD-2022-15951

Vulnerability from cnvd - Published: 2022-03-02
VLAI
Title
libsolv堆溢出漏洞(CNVD-2022-15951)
Description
libsolv是一个用于检查软件包依赖的库。 libsolv存在堆溢出漏洞,该漏洞源于libsolv中存在两个内存漏洞位于src/solver.c的resolve_weak函数。目前没有详细的漏洞细节提供。
Severity
Patch Name
libsolv堆溢出漏洞(CNVD-2022-15951)的补丁
Patch Description
libsolv是一个用于检查软件包依赖的库。 libsolv存在堆溢出漏洞,该漏洞源于libsolv中存在两个内存漏洞位于src/solver.c的resolve_weak函数。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/openSUSE/libsolv/issues/426

Reference
https://nvd.nist.gov/vuln/detail/CVE-2021-44576
Impacted products
Name
['openSUSE Libsolv 0.6.4', 'openSUSE Libsolv 0.6.5', 'openSUSE Libsolv 0.6.6', 'openSUSE Libsolv 0.6.7', 'openSUSE Libsolv 0.6.8', 'openSUSE Libsolv 0.6.9', 'openSUSE Libsolv 0.6.10', 'openSUSE Libsolv 0.6.11', 'openSUSE Libsolv 0.6.12', 'openSUSE Libsolv 0.6.13']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-44576"
    }
  },
  "description": "libsolv\u662f\u4e00\u4e2a\u7528\u4e8e\u68c0\u67e5\u8f6f\u4ef6\u5305\u4f9d\u8d56\u7684\u5e93\u3002\n\nlibsolv\u5b58\u5728\u5806\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8elibsolv\u4e2d\u5b58\u5728\u4e24\u4e2a\u5185\u5b58\u6f0f\u6d1e\u4f4d\u4e8esrc/solver.c\u7684resolve_weak\u51fd\u6570\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/openSUSE/libsolv/issues/426",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-15951",
  "openTime": "2022-03-02",
  "patchDescription": "libsolv\u662f\u4e00\u4e2a\u7528\u4e8e\u68c0\u67e5\u8f6f\u4ef6\u5305\u4f9d\u8d56\u7684\u5e93\u3002\r\n\r\nlibsolv\u5b58\u5728\u5806\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8elibsolv\u4e2d\u5b58\u5728\u4e24\u4e2a\u5185\u5b58\u6f0f\u6d1e\u4f4d\u4e8esrc/solver.c\u7684resolve_weak\u51fd\u6570\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": "libsolv\u5806\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2022-15951\uff09\u7684\u8865\u4e01",
  "products": {
    "product": [
      "openSUSE Libsolv 0.6.4",
      "openSUSE Libsolv 0.6.5",
      "openSUSE Libsolv 0.6.6",
      "openSUSE Libsolv 0.6.7",
      "openSUSE Libsolv 0.6.8",
      "openSUSE Libsolv 0.6.9",
      "openSUSE Libsolv 0.6.10",
      "openSUSE Libsolv 0.6.11",
      "openSUSE Libsolv 0.6.12",
      "openSUSE Libsolv 0.6.13"
    ]
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-44576",
  "serverity": "\u4e2d",
  "submitTime": "2022-02-23",
  "title": "libsolv\u5806\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2022-15951\uff09"
}



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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

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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.

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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.


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