CNVD-2022-04507

Vulnerability from cnvd - Published: 2022-01-17
VLAI
Title
Binaryen堆栈缓冲区溢出漏洞
Description
Binaryen是一款使用C++编写的用于WebAssembly的编译器基础结构和工具链库。 Binaryen在103版本存在堆栈缓冲区溢出漏洞,该漏洞源于软件printf公共函数在内存上执行操作时,攻击者可利用该漏洞导致向关联的其他内存位置上执行了错误的读写操作。
Severity
Patch Name
Binaryen堆栈缓冲区溢出漏洞的补丁
Patch Description
Binaryen是一款使用C++编写的用于WebAssembly的编译器基础结构和工具链库。 Binaryen在103版本存在堆栈缓冲区溢出漏洞,该漏洞源于软件printf公共函数在内存上执行操作时,攻击者可利用该漏洞导致向关联的其他内存位置上执行了错误的读写操作。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

厂商尚未提供漏洞修复方案,请关注厂商主页更新: https://github.com/WebAssembly/binaryen/issues/4391

Reference
https://github.com/WebAssembly/binaryen/issues/4391
Impacted products
Name
WebAssembly Binaryen 103
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-46050",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-46050"
    }
  },
  "description": "Binaryen\u662f\u4e00\u6b3e\u4f7f\u7528C++\u7f16\u5199\u7684\u7528\u4e8eWebAssembly\u7684\u7f16\u8bd1\u5668\u57fa\u7840\u7ed3\u6784\u548c\u5de5\u5177\u94fe\u5e93\u3002\n\nBinaryen\u5728103\u7248\u672c\u5b58\u5728\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8f6f\u4ef6printf\u516c\u5171\u51fd\u6570\u5728\u5185\u5b58\u4e0a\u6267\u884c\u64cd\u4f5c\u65f6\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u5411\u5173\u8054\u7684\u5176\u4ed6\u5185\u5b58\u4f4d\u7f6e\u4e0a\u6267\u884c\u4e86\u9519\u8bef\u7684\u8bfb\u5199\u64cd\u4f5c\u3002",
  "formalWay": "\u5382\u5546\u5c1a\u672a\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u590d\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u66f4\u65b0\uff1a\r\nhttps://github.com/WebAssembly/binaryen/issues/4391",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-04507",
  "openTime": "2022-01-17",
  "patchDescription": "Binaryen\u662f\u4e00\u6b3e\u4f7f\u7528C++\u7f16\u5199\u7684\u7528\u4e8eWebAssembly\u7684\u7f16\u8bd1\u5668\u57fa\u7840\u7ed3\u6784\u548c\u5de5\u5177\u94fe\u5e93\u3002\r\n\r\nBinaryen\u5728103\u7248\u672c\u5b58\u5728\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8f6f\u4ef6printf\u516c\u5171\u51fd\u6570\u5728\u5185\u5b58\u4e0a\u6267\u884c\u64cd\u4f5c\u65f6\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u5411\u5173\u8054\u7684\u5176\u4ed6\u5185\u5b58\u4f4d\u7f6e\u4e0a\u6267\u884c\u4e86\u9519\u8bef\u7684\u8bfb\u5199\u64cd\u4f5c\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": "Binaryen\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "WebAssembly Binaryen 103"
  },
  "referenceLink": "https://github.com/WebAssembly/binaryen/issues/4391",
  "serverity": "\u4e2d",
  "submitTime": "2022-01-14",
  "title": "Binaryen\u5806\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e"
}



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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

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.

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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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