Vulnerability-Lookup

CNVD-2019-26796

Vulnerability from cnvd - Published: 2019-08-12

Title

CloudBees Jenkins Credentials Binding Plugin Jenkins插件信息泄露漏洞

Description

CloudBees Jenkins（Hudson Labs）是美国CloudBees公司的一套基于Java开发的持续集成工具。该产品主要用于监控持续的软件版本发布/测试项目和一些定时执行的任务。Jenkins Credentials Binding Plugin是使用在其中的一个凭证绑定插件。 CloudBees Jenkins Credentials Binding Plugin Jenkins 1.17版本中的config-variables.jelly文件存在安全漏洞，该漏洞源于程序将密码存储为可恢复的格式。攻击者可利用该漏洞恢复凭证。

Severity

中

Formal description

目前厂商暂未发布修复措施解决此安全问题，建议使用此软件的用户随时关注厂商主页或参考网址以获取解决办法： https://jenkins.io/

Reference

https://nvd.nist.gov/vuln/detail/CVE-2019-1010241

Impacted products

Name
CloudBees Jenkins Credentials Binding Plugin Jenkins 1.17

Show details on source website

JSON

To clipboard

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    "cve": {
      "cveNumber": "CVE-2019-1010241"
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  "discovererName": "unknwon",
  "formalWay": "\u76ee\u524d\u5382\u5546\u6682\u672a\u53d1\u5e03\u4fee\u590d\u63aa\u65bd\u89e3\u51b3\u6b64\u5b89\u5168\u95ee\u9898\uff0c\u5efa\u8bae\u4f7f\u7528\u6b64\u8f6f\u4ef6\u7684\u7528\u6237\u968f\u65f6\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u6216\u53c2\u8003\u7f51\u5740\u4ee5\u83b7\u53d6\u89e3\u51b3\u529e\u6cd5\uff1a\r\nhttps://jenkins.io/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-26796",
  "openTime": "2019-08-12",
  "products": {
    "product": "CloudBees Jenkins Credentials Binding Plugin Jenkins 1.17"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-1010241",
  "serverity": "\u4e2d",
  "submitTime": "2019-07-22",
  "title": "CloudBees Jenkins Credentials Binding Plugin Jenkins\u63d2\u4ef6\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e"
}

CVE-2019-1010241 (GCVE-0-2019-1010241)

Vulnerability from cvelistv5 – Published: 2019-07-19 16:36 – Updated: 2024-08-05 03:07

Summary

Jenkins Credentials Binding Plugin Jenkins 1.17 is affected by: CWE-257: Storing Passwords in a Recoverable Format. The impact is: Authenticated users can recover credentials. The component is: config-variables.jelly line #30 (passwordVariable). The attack vector is: Attacker creates and executes a Jenkins job.

Severity

No CVSS data available.

CWE

CWE-257 - Storing Passwords in a Recoverable Format

Assigner

dwf

References

2 references

URL	Tags
https://docs.google.com/document/d/1MBEoJSMvkjp5K…	x_refsource_MISC
http://www.securityfocus.com/bid/109320	vdb-entryx_refsource_BID

Impacted products

1 product

Vendor	Product	Version
Jenkins Credentials Binding Plugin	Jenkins	Affected: 1.17

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.