Vulnerability-Lookup

CNVD-2022-58303

Vulnerability from cnvd - Published: 2022-08-15

Title

ZZCMS SQL注入漏洞（CNVD-2022-58303）

Description

ZZCMS是中国ZZCMS团队的一套内容管理系统（CMS）。 ZZCMS 2019版本存在安全漏洞，攻击者利用该漏洞可通过/admin/ztliuyan_sendmail.php中id参数执行SQL注入攻击。

Severity

中

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/forget-code/zzcms

Reference

https://nvd.nist.gov/vuln/detail/CVE-2019-12359

Impacted products

Name
ZZCMS ZZCMS 2019

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2019-12359",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2019-12359"
    }
  },
  "description": "ZZCMS\u662f\u4e2d\u56fdZZCMS\u56e2\u961f\u7684\u4e00\u5957\u5185\u5bb9\u7ba1\u7406\u7cfb\u7edf\uff08CMS\uff09\u3002\n\nZZCMS 2019\u7248\u672c\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u5229\u7528\u8be5\u6f0f\u6d1e\u53ef\u901a\u8fc7/admin/ztliuyan_sendmail.php\u4e2did\u53c2\u6570\u6267\u884cSQL\u6ce8\u5165\u653b\u51fb\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/forget-code/zzcms",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-58303",
  "openTime": "2022-08-15",
  "products": {
    "product": "ZZCMS ZZCMS 2019"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-12359",
  "serverity": "\u4e2d",
  "submitTime": "2022-06-21",
  "title": "ZZCMS SQL\u6ce8\u5165\u6f0f\u6d1e\uff08CNVD-2022-58303\uff09"
}

CVE-2019-12359 (GCVE-0-2019-12359)

Vulnerability from cvelistv5 – Published: 2022-06-17 12:09 – Updated: 2024-08-04 23:17

Summary

An issue was discovered in zzcms 2019. There is a SQL injection Vulnerability in /admin/ztliuyan_sendmail.php (when the attacker has admin authority) via the id parameter.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

1 reference

URL	Tags
https://github.com/cby234/zzcms/issues/5	x_refsource_MISC

Show details on NVD website

JSON

To clipboard

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          "dateUpdated": "2024-08-04T23:17:39.964Z",
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              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/cby234/zzcms/issues/5"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
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          "versions": [
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      },
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        {
          "tags": [
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          "url": "https://github.com/cby234/zzcms/issues/5"
        }
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          "ASSIGNER": "cve@mitre.org",
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  "cveMetadata": {
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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.