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

CNVD-2021-91643

Vulnerability from cnvd - Published: 2021-11-26
VLAI
Title
fcovatti libiec_iccp_mod缓冲区溢出漏洞(CNVD-2021-91643)
Description
Libiec_Iccp_Mod是用于修改 Libiec6850 Mms 以使用 Iccp 客户端。 fcovatti libiec_iccp_mod 中存在缓冲区错误漏洞,该漏洞源于产品未能正确处理某些特殊的数据包。攻击者可通过该漏洞导致拒绝服务。
Severity
Patch Name
fcovatti libiec_iccp_mod缓冲区溢出漏洞(CNVD-2021-91643)的补丁
Patch Description
Libiec_Iccp_Mod是用于修改 Libiec6850 Mms 以使用 Iccp 客户端。 fcovatti libiec_iccp_mod 中存在缓冲区错误漏洞,该漏洞源于产品未能正确处理某些特殊的数据包。攻击者可通过该漏洞导致拒绝服务。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/fcovatti/libiec_iccp_mod

Reference
https://nvd.nist.gov/vuln/detail/CVE-2020-20657
Impacted products
Name
Libiec_Iccp_Mod fcovatti libiec_iccp_mod 1.5
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-20657"
    }
  },
  "description": "Libiec_Iccp_Mod\u662f\u7528\u4e8e\u4fee\u6539 Libiec6850 Mms \u4ee5\u4f7f\u7528 Iccp \u5ba2\u6237\u7aef\u3002\n\nfcovatti libiec_iccp_mod \u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u4ea7\u54c1\u672a\u80fd\u6b63\u786e\u5904\u7406\u67d0\u4e9b\u7279\u6b8a\u7684\u6570\u636e\u5305\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\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/fcovatti/libiec_iccp_mod",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-91643",
  "openTime": "2021-11-26",
  "patchDescription": "Libiec_Iccp_Mod\u662f\u7528\u4e8e\u4fee\u6539 Libiec6850 Mms \u4ee5\u4f7f\u7528 Iccp \u5ba2\u6237\u7aef\u3002\r\n\r\nfcovatti libiec_iccp_mod \u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u4ea7\u54c1\u672a\u80fd\u6b63\u786e\u5904\u7406\u67d0\u4e9b\u7279\u6b8a\u7684\u6570\u636e\u5305\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\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": "fcovatti libiec_iccp_mod\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2021-91643\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "Libiec_Iccp_Mod fcovatti libiec_iccp_mod 1.5"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-20657",
  "serverity": "\u4e2d",
  "submitTime": "2021-11-03",
  "title": "fcovatti libiec_iccp_mod\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2021-91643\uff09"
}



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…