Vulnerability-Lookup

CNVD-2021-24249

Vulnerability from cnvd - Published: 2021-04-01

Title

Apache Superset资源管理错误漏洞

Description

Apache Superset是美国阿帕奇（Apache）软件基金会的一套企业级商业智能Web应用程序。该程序具有数据收集、数据可视化和身份验证等功能。 Apache Superset 0.37之前版本存在资源管理错误漏洞，该问题源于web程序可任意访问os包，攻击者可利用该漏洞操作任意文件。

Severity

高

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://lists.apache.org/thread.html/rdeee068ac1e0c43bd5b69830240f30598df15a2ef9f7998c7b29131e%40%3Cdev.superset.apache.org%3E

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-13948

Impacted products

Name
Apache Superset <0.37

Show details on source website

JSON

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      "cveNumber": "CVE-2020-13948",
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  "number": "CNVD-2021-24249",
  "openTime": "2021-04-01",
  "products": {
    "product": "Apache Superset \u003c0.37"
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-13948",
  "serverity": "\u9ad8",
  "submitTime": "2020-09-18",
  "title": "Apache Superset\u8d44\u6e90\u7ba1\u7406\u9519\u8bef\u6f0f\u6d1e"
}

CVE-2020-13948 (GCVE-0-2020-13948)

Vulnerability from cvelistv5 – Published: 2020-09-17 12:31 – Updated: 2024-08-04 12:32

Summary

While investigating a bug report on Apache Superset, it was determined that an authenticated user could craft requests via a number of templated text fields in the product that would allow arbitrary access to Python’s `os` package in the web application process in versions < 0.37.1. It was thus possible for an authenticated user to list and access files, environment variables, and process information. Additionally it was possible to set environment variables for the current process, create and update files in folders writable by the web process, and execute arbitrary programs accessible by the web process. All other operations available to the `os` package in Python were also available, even if not explicitly enumerated in this CVE.

Severity

No CVSS data available.

CWE

Remote Code Execution Vulnerability

Assigner

apache CNA under the mitre root

CNA scorecard B 76/100 over 1085 records in the last 180 days details

References

3 references

URL	Tags
https://lists.apache.org/thread.html/rdeee068ac1e…	x_refsource_MISC
https://lists.apache.org/thread.html/r4fc7115f6e6…	mailing-listx_refsource_MLIST
https://lists.apache.org/thread.html/r0e35c7c5672…	mailing-listx_refsource_MLIST

Impacted products

1 product

Vendor	Product	Version
n/a	Apache Superset	Affected: Apache Superset < 0.37.1

Show details on NVD website

JSON

To clipboard

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          },
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          "vendor": "n/a",
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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.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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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.

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