Vulnerability-Lookup

CNVD-2018-09638

Vulnerability from cnvd - Published: 2018-05-17

Title

Pivotal Spring Data Commons任意文件读取漏洞

Description

Pivotal Spring Data Commons是美国Pivotal Software公司的一个为数据访问提供基于Spring模型的项目。 Pivotal Spring Data Commons 1.13.12之前的1.13版本和2.0.7之前的2.0版本中存在安全漏洞，该漏洞源于程序未能正确的限制XML外部实体的引用。当与XMLBeam 1.4.14及之前版本一起使用时，远程攻击者可通过提交特制的请求参数利用该漏洞访问系统上的任意文件。

Severity

中

Patch Name

Pivotal Spring Data Commons任意文件读取漏洞的补丁

Patch Description

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://pivotal.io/security/cve-2018-1259

Reference

https://pivotal.io/security/cve-2018-1259

Impacted products

Name
['Pivotal Software Spring Data Commons 1.13，<1.13.12', 'Pivotal Software Spring Data Commons 2.0，<2.0.7']

Show details on source website

JSON

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    "cve": {
      "cveNumber": "CVE-2018-1259"
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  "discovererName": "the Snyk Security Research Team",
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  "number": "CNVD-2018-09638",
  "openTime": "2018-05-17",
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  "patchName": "Pivotal Spring Data Commons\u4efb\u610f\u6587\u4ef6\u8bfb\u53d6\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
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  "referenceLink": "https://pivotal.io/security/cve-2018-1259",
  "serverity": "\u4e2d",
  "submitTime": "2018-05-15",
  "title": "Pivotal Spring Data Commons\u4efb\u610f\u6587\u4ef6\u8bfb\u53d6\u6f0f\u6d1e"
}

CVE-2018-1259 (GCVE-0-2018-1259)

Vulnerability from cvelistv5 – Published: 2018-05-11 20:00 – Updated: 2024-09-16 16:33

Summary

Spring Data Commons, versions 1.13 prior to 1.13.12 and 2.0 prior to 2.0.7, used in combination with XMLBeam 1.4.14 or earlier versions, contains a property binder vulnerability caused by improper restriction of XML external entity references as underlying library XMLBeam does not restrict external reference expansion. An unauthenticated remote malicious user can supply specially crafted request parameters against Spring Data's projection-based request payload binding to access arbitrary files on the system.

Severity

No CVSS data available.

CWE

XML Parsing

Assigner

dell mitre root

References

4 references

URL	Tags
https://access.redhat.com/errata/RHSA-2018:1809	vendor-advisoryx_refsource_REDHAT
https://access.redhat.com/errata/RHSA-2018:3768	vendor-advisoryx_refsource_REDHAT
https://www.oracle.com/security-alerts/cpujul2022.html	x_refsource_MISC
https://pivotal.io/security/cve-2018-1259	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
Pivotal	Spring Data Commons	Affected: 1.13 prior to 1.13.12; 2.0 prior to 2.0.7

Date Public

2018-05-09 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.