Vulnerability-Lookup

CNVD-2024-27496

Vulnerability from cnvd - Published: 2024-06-18

Title

Apache Arrow反序列化漏洞

Description

Apache Arrow是美国阿帕奇（Apache）基金会的一款用于内存数据处理的跨语言开发平台。该平台支持C、C++、C＃、Go和Java等编程语言，并提供进程间通信等功能。 Apache Arrow 0.14.0版本至14.0.0版本存在反序列化漏洞，该漏洞源于应用程序在接收用户提交的序列化数据的不安全反序列化处理，攻击者可利用该漏洞导致代码执行。

Severity

高

Patch Name

Apache Arrow反序列化漏洞的补丁

Patch Description

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://lists.apache.org/thread/yhy7tdfjf9hrl9vfrtzo8p2cyjq87v7n

Reference

https://nvd.nist.gov/vuln/detail/CVE-2023-47248

Impacted products

Name
Apache Apache Arrow >=0.14.0，<=14.0.0

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2023-47248",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2023-47248"
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  "number": "CNVD-2024-27496",
  "openTime": "2024-06-18",
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  "patchName": "Apache Arrow\u53cd\u5e8f\u5217\u5316\u6f0f\u6d1e\u7684\u8865\u4e01",
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2023-47248",
  "serverity": "\u9ad8",
  "submitTime": "2023-11-13",
  "title": "Apache Arrow\u53cd\u5e8f\u5217\u5316\u6f0f\u6d1e"
}

CVE-2023-47248 (GCVE-0-2023-47248)

Vulnerability from cvelistv5 – Published: 2023-11-09 08:17 – Updated: 2025-02-13 17:17

Title

PyArrow, PyArrow: Arbitrary code execution when loading a malicious data file

Summary

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings. It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon. If it is not possible to upgrade, we provide a separate package `pyarrow-hotfix` that disables the vulnerability on older PyArrow versions. See https://pypi.org/project/pyarrow-hotfix/ for instructions.

Severity

No CVSS data available.

CWE

CWE-502 - Deserialization of Untrusted Data

Assigner

apache mitre root

References

6 references

URL	Tags
https://lists.apache.org/thread/yhy7tdfjf9hrl9vfr…	vendor-advisory
https://github.com/apache/arrow/commit/f141709763…	patch
https://pypi.org/project/pyarrow-hotfix/	mitigation
https://lists.fedoraproject.org/archives/list/pac…
https://lists.fedoraproject.org/archives/list/pac…
https://lists.fedoraproject.org/archives/list/pac…

Impacted products

1 product

Vendor	Product	Version
Apache Software Foundation	PyArrow	Affected: 0.14.0 , ≤ 14.0.0 (semver)

Credits

Li Jiakun - laoquanshi

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.