Vulnerability-Lookup

CNVD-2022-16402

Vulnerability from cnvd - Published: 2022-03-03

Title

Spring Cloud Gateway远程代码执行漏洞

Description

Spring Cloud GateWay是提供了一个用于在Spring WebFlux之上构建API网关的库。 Spring Cloud Gateway存在远程代码执行漏洞，该漏洞是发生在Spring Cloud Gateway应用程序的Actuator端点，其在启用、公开和不安全的情况下容易受到代码注入的攻击。攻击者可利用该漏洞通过恶意创建允许在远程主机上执行任意远程请求。

Severity

高

Patch Name

Spring Cloud Gateway远程代码执行漏洞的补丁

Patch Description

Formal description

用户可联系供应商获得补丁信息： https://spring.io/blog/2022/03/01/spring-cloud-gateway-cve-reports-published

Reference

https://spring.io/blog/2022/03/01/spring-cloud-gateway-cve-reports-published

Impacted products

Name
['VMWare Spring Cloud GateWay 3.1.0', 'VMWare Spring Cloud GateWay >=3.0.0，<=3.0.6', 'VMWare Spring Cloud GateWay <3.0.0']

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CVE-2022-22947 (GCVE-0-2022-22947)

Vulnerability from cvelistv5 – Published: 2022-03-03 00:00 – Updated: 2025-10-21 23:15

Summary

In spring cloud gateway versions prior to 3.1.1+ and 3.0.7+ , applications are vulnerable to a code injection attack when the Gateway Actuator endpoint is enabled, exposed and unsecured. A remote attacker could make a maliciously crafted request that could allow arbitrary remote execution on the remote host.

Severity

10 (Critical)


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

SSVC

Exploitation: active Automatable: yes Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-01-29 17:53 UTC

CWE

CWE-94 - Improper Control of Generation of Code ('Code Injection')

Assigner

vmware mitre root

References

6 references

URL	Tags
https://tanzu.vmware.com/security/cve-2022-22947
https://www.oracle.com/security-alerts/cpuapr2022.html
http://packetstormsecurity.com/files/166219/Sprin…
https://www.oracle.com/security-alerts/cpujul2022.html
http://packetstormsecurity.com/files/168742/Sprin…
https://www.cisa.gov/known-exploited-vulnerabilit…	government-resource

Impacted products

1 product

Vendor	Product	Version
n/a	Spring Cloud Gateway	Affected: Spring cloud gateway versions 3.1.x prior to 3.1.1+, 3.0.x prior to 3.0.7+ and all old and unsupported versions

Show details on NVD website

JSON

To clipboard

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      {
        "metrics": [
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              "attackComplexity": "LOW",
              "attackVector": "NETWORK",
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              "baseScore": 10,
              "baseSeverity": "CRITICAL",
              "confidentialityImpact": "HIGH",
              "integrityImpact": "HIGH",
              "privilegesRequired": "NONE",
              "scope": "CHANGED",
              "userInteraction": "NONE",
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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.