Vulnerability-Lookup

CNVD-2023-37166

Vulnerability from cnvd - Published: 2023-05-15

Title

IBM Cloud Pak for Data服务端请求伪造漏洞

Description

IBM Cloud Pak for Data是美国国际商业机器（IBM）公司的一个具有数据结构的数据和AI平台。可以在任何云上为AI和分析提供所有数据。 IBM Cloud Pak for Data 4.0和4.5版本存在服务端请求伪造漏洞，该漏洞源于产品未能正确验证用户输入，经过身份验证的攻击者可利用该漏洞从系统发送未经授权的请求，从而可能导致网络枚举或促进其他攻击。

Severity

中

Patch Name

IBM Cloud Pak for Data服务端请求伪造漏洞的补丁

Patch Description

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://www.ibm.com/support/pages/node/6985859

Reference

https://nvd.nist.gov/vuln/detail/CVE-2023-30444

Impacted products

Name
['IBM Cloud Pak for Data 4.5', 'IBM Cloud Pak for Data 4.0']

Show details on source website

JSON

To clipboard

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      "cveNumber": "CVE-2023-30444",
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}

CVE-2023-30444 (GCVE-0-2023-30444)

Vulnerability from cvelistv5 – Published: 2023-04-27 12:52 – Updated: 2025-01-30 20:59

Title

IBM Watson Machine Learning on Cloud Pak for Data server-side request forgery

Summary

IBM Watson Machine Learning on Cloud Pak for Data 4.0 and 4.5 is vulnerable to server-side request forgery (SSRF). This may allow an authenticated attacker to send unauthorized requests from the system, potentially leading to network enumeration or facilitating other attacks. IBM X-Force ID: 253350.

Severity

7.1 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-01-30 20:59 UTC

CWE

CWE-918 - Server-Side Request Forgery (SSRF)

Assigner

ibm mitre root

References

1 reference

URL	Tags
https://www.ibm.com/support/pages/node/6985859	vendor-advisory

Impacted products

1 product

Vendor	Product	Version
IBM	Watson Machine Learning on Cloud Pak for Data	Affected: 4.0, 4.5

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.