Vulnerability-Lookup

CNVD-2019-26809

Vulnerability from cnvd - Published: 2019-08-12

Title

Teclib GLPI存在未明漏洞

Description

Teclib GLPI是法国Teclib公司的一套开源的IT资产管理套件。该套件包含设备状态管理、资产清单存储、管理流程和工作日志管理等功能。 Teclib GLPI 9.3.1版本中存在安全漏洞。攻击者可利用该漏洞实施钓鱼攻击，获取用户的凭证/信用卡信息。

Severity

低

Patch Name

Teclib GLPI存在未明漏洞的补丁

Patch Description

Teclib GLPI是法国Teclib公司的一套开源的IT资产管理套件。该套件包含设备状态管理、资产清单存储、管理流程和工作日志管理等功能。 Teclib GLPI 9.3.1版本中存在安全漏洞。攻击者可利用该漏洞实施钓鱼攻击，获取用户的凭证/信用卡信息。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/glpi-project/glpi/pull/5519

Reference

https://nvd.nist.gov/vuln/detail/CVE-2019-1010310

Impacted products

Name
Teclib Teclib GLPI 9.3.1

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2019-1010310"
    }
  },
  "description": "Teclib GLPI\u662f\u6cd5\u56fdTeclib\u516c\u53f8\u7684\u4e00\u5957\u5f00\u6e90\u7684IT\u8d44\u4ea7\u7ba1\u7406\u5957\u4ef6\u3002\u8be5\u5957\u4ef6\u5305\u542b\u8bbe\u5907\u72b6\u6001\u7ba1\u7406\u3001\u8d44\u4ea7\u6e05\u5355\u5b58\u50a8\u3001\u7ba1\u7406\u6d41\u7a0b\u548c\u5de5\u4f5c\u65e5\u5fd7\u7ba1\u7406\u7b49\u529f\u80fd\u3002\n\nTeclib GLPI 9.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5b9e\u65bd\u9493\u9c7c\u653b\u51fb\uff0c\u83b7\u53d6\u7528\u6237\u7684\u51ed\u8bc1/\u4fe1\u7528\u5361\u4fe1\u606f\u3002",
  "discovererName": "unknwon",
  "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/glpi-project/glpi/pull/5519",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-26809",
  "openTime": "2019-08-12",
  "patchDescription": "Teclib GLPI\u662f\u6cd5\u56fdTeclib\u516c\u53f8\u7684\u4e00\u5957\u5f00\u6e90\u7684IT\u8d44\u4ea7\u7ba1\u7406\u5957\u4ef6\u3002\u8be5\u5957\u4ef6\u5305\u542b\u8bbe\u5907\u72b6\u6001\u7ba1\u7406\u3001\u8d44\u4ea7\u6e05\u5355\u5b58\u50a8\u3001\u7ba1\u7406\u6d41\u7a0b\u548c\u5de5\u4f5c\u65e5\u5fd7\u7ba1\u7406\u7b49\u529f\u80fd\u3002\r\n\r\nTeclib GLPI 9.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5b9e\u65bd\u9493\u9c7c\u653b\u51fb\uff0c\u83b7\u53d6\u7528\u6237\u7684\u51ed\u8bc1/\u4fe1\u7528\u5361\u4fe1\u606f\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": "Teclib GLPI\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Teclib Teclib GLPI 9.3.1"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-1010310",
  "serverity": "\u4f4e",
  "submitTime": "2019-07-17",
  "title": "Teclib GLPI\u5b58\u5728\u672a\u660e\u6f0f\u6d1e"
}

CVE-2019-1010310 (GCVE-0-2019-1010310)

Vulnerability from cvelistv5 – Published: 2019-07-12 17:45 – Updated: 2024-08-05 03:07

Summary

GLPI GLPI Product 9.3.1 is affected by: Frame and Form tags Injection allowing admins to phish users by putting code in reminder description. The impact is: Admins can phish any user or group of users for credentials / credit cards. The component is: Tools > Reminder > Description .. Set the description to any iframe/form tags and apply. The attack vector is: The attacker puts a login form, the user fills it and clicks on submit .. the request is sent to the attacker domain saving the data. The fixed version is: 9.4.1.

Severity

No CVSS data available.

CWE

Frame and Form tags Injection allowing admins to phish users by putting code in reminder description

Assigner

dwf

References

2 references

URL	Tags
https://github.com/glpi-project/glpi/releases/tag/9.3.1	x_refsource_MISC
https://github.com/glpi-project/glpi/pull/5519	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
GLPI	GLPI Product	Affected: 9.3.1 [fixed: 9.4.1]

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-05T03:07:18.662Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/glpi-project/glpi/releases/tag/9.3.1"
          },
          {
            "tags": [
              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/glpi-project/glpi/pull/5519"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "GLPI Product",
          "vendor": "GLPI",
          "versions": [
            {
              "status": "affected",
              "version": "9.3.1 [fixed: 9.4.1]"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "GLPI GLPI Product 9.3.1 is affected by: Frame and Form tags Injection allowing admins to phish users by putting code in reminder description. The impact is: Admins can phish any user or group of users for credentials / credit cards. The component is: Tools \u003e Reminder \u003e Description .. Set the description to any iframe/form tags and apply. The attack vector is: The attacker puts a login form, the user fills it and clicks on submit .. the request is sent to the attacker domain saving the data. The fixed version is: 9.4.1."
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "description": "Frame and Form tags Injection allowing admins to phish users by putting code in reminder description",
              "lang": "en",
              "type": "text"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2019-07-12T17:45:56.000Z",
        "orgId": "7556d962-6fb7-411e-85fa-6cd62f095ba8",
        "shortName": "dwf"
      },
      "references": [
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/glpi-project/glpi/releases/tag/9.3.1"
        },
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/glpi-project/glpi/pull/5519"
        }
      ],
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "cve-assign@distributedweaknessfiling.org",
          "ID": "CVE-2019-1010310",
          "STATE": "PUBLIC"
        },
        "affects": {
          "vendor": {
            "vendor_data": [
              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "GLPI Product",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "9.3.1 [fixed: 9.4.1]"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "GLPI"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
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            {
              "lang": "eng",
              "value": "GLPI GLPI Product 9.3.1 is affected by: Frame and Form tags Injection allowing admins to phish users by putting code in reminder description. The impact is: Admins can phish any user or group of users for credentials / credit cards. The component is: Tools \u003e Reminder \u003e Description .. Set the description to any iframe/form tags and apply. The attack vector is: The attacker puts a login form, the user fills it and clicks on submit .. the request is sent to the attacker domain saving the data. The fixed version is: 9.4.1."
            }
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        },
        "problemtype": {
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                  "value": "Frame and Form tags Injection allowing admins to phish users by putting code in reminder description"
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        "references": {
          "reference_data": [
            {
              "name": "https://github.com/glpi-project/glpi/releases/tag/9.3.1",
              "refsource": "MISC",
              "url": "https://github.com/glpi-project/glpi/releases/tag/9.3.1"
            },
            {
              "name": "https://github.com/glpi-project/glpi/pull/5519",
              "refsource": "MISC",
              "url": "https://github.com/glpi-project/glpi/pull/5519"
            }
          ]
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "7556d962-6fb7-411e-85fa-6cd62f095ba8",
    "assignerShortName": "dwf",
    "cveId": "CVE-2019-1010310",
    "datePublished": "2019-07-12T17:45:56.000Z",
    "dateReserved": "2019-03-20T00:00:00.000Z",
    "dateUpdated": "2024-08-05T03:07:18.662Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

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.