Vulnerability-Lookup

CNVD-2017-00857

Vulnerability from cnvd - Published: 2017-02-04

Title

ZoneMinder存在多个漏洞

Description

ZoneMinder是一套开源的视频监控系统。 ZoneMinder 1.30.0版本中的Apache HTTP服务器配置存在信息泄露和认证绕过漏洞。未经身份验证的远程攻击者可利用漏洞浏览所有web根目录。

Severity

中

Formal description

目前没有详细的解决方案提供： https://www.zoneminder.com/

Reference

https://github.com/ZoneMinder/ZoneMinder/pull/1697

Impacted products

Name
['Triornis ZoneMinder', 'Zoneminder Zoneminder 1.30.0']

Show details on source website

JSON

To clipboard

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  "cves": {
    "cve": {
      "cveNumber": "CVE-2016-10140"
    }
  },
  "description": "ZoneMinder\u662f\u4e00\u5957\u5f00\u6e90\u7684\u89c6\u9891\u76d1\u63a7\u7cfb\u7edf\u3002\r\n\r\nZoneMinder 1.30.0\u7248\u672c\u4e2d\u7684Apache HTTP\u670d\u52a1\u5668\u914d\u7f6e\u5b58\u5728\u4fe1\u606f\u6cc4\u9732\u548c\u8ba4\u8bc1\u7ed5\u8fc7\u6f0f\u6d1e\u3002\u672a\u7ecf\u8eab\u4efd\u9a8c\u8bc1\u7684\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u6d4f\u89c8\u6240\u6709web\u6839\u76ee\u5f55\u3002",
  "discovererName": "unknown",
  "formalWay": "\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u89e3\u51b3\u65b9\u6848\u63d0\u4f9b\uff1a\r\nhttps://www.zoneminder.com/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-00857",
  "openTime": "2017-02-04",
  "products": {
    "product": [
      "Triornis ZoneMinder",
      "Zoneminder Zoneminder 1.30.0"
    ]
  },
  "referenceLink": "https://github.com/ZoneMinder/ZoneMinder/pull/1697",
  "serverity": "\u4e2d",
  "submitTime": "2017-01-18",
  "title": "ZoneMinder\u5b58\u5728\u591a\u4e2a\u6f0f\u6d1e"
}

CVE-2016-10140 (GCVE-0-2016-10140)

Vulnerability from cvelistv5 – Published: 2017-01-13 09:00 – Updated: 2024-08-06 03:14

Summary

Information disclosure and authentication bypass vulnerability exists in the Apache HTTP Server configuration bundled with ZoneMinder v1.30 and v1.29, which allows a remote unauthenticated attacker to browse all directories in the web root, e.g., a remote unauthenticated attacker can view all CCTV images on the server via the /events URI.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

5 references

URL	Tags
http://seclists.org/fulldisclosure/2017/Feb/11	x_refsource_MISC
http://www.securityfocus.com/bid/96849	vdb-entryx_refsource_BID
https://github.com/ZoneMinder/ZoneMinder/commit/7…	x_refsource_CONFIRM
https://github.com/ZoneMinder/ZoneMinder/pull/1697	x_refsource_CONFIRM
http://seclists.org/bugtraq/2017/Feb/6	x_refsource_MISC

Date Public

2017-01-13 00:00

Show details on NVD website

JSON

To clipboard

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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.