Vulnerability-Lookup

CNVD-2022-38530

Vulnerability from cnvd - Published: 2022-05-19

Title

Apache Apisix输入验证错误漏洞

Description

Apache Apisix是美国阿帕奇（Apache）基金会的一个云原生的微服务API网关服务。该软件基于 OpenResty 和 etcd 来实现，具备动态路由和插件热加载，适合微服务体系下的 API 管理。 Apache Apisix 2.13.0之前版本存在输入验证错误漏洞，该漏洞源于当解码带有重复键的 JSON 时，lua-cjson 会选择最后出现的值作为结果。攻击者可利用该漏洞绕过request-validation插件中的body_schema验证。

Severity

中

Patch Name

Apache Apisix输入验证错误漏洞的补丁

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/03vd2j81krxmpz6xo8p1dl642flpo6fv

Reference

https://nvd.nist.gov/vuln/detail/CVE-2022-25757

Impacted products

Name
Apache Apache APISIX <2.13.0

Show details on source website

JSON

To clipboard

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    "cve": {
      "cveNumber": "CVE-2022-25757",
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  "number": "CNVD-2022-38530",
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  "serverity": "\u4e2d",
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}

CVE-2022-25757 (GCVE-0-2022-25757)

Vulnerability from cvelistv5 – Published: 2022-03-28 07:00 – Updated: 2024-08-03 04:49

Title

Apache APISIX: the body_schema check in request-validation plugin can be bypassed

Summary

In Apache APISIX before 2.13.0, when decoding JSON with duplicate keys, lua-cjson will choose the last occurred value as the result. By passing a JSON with a duplicate key, the attacker can bypass the body_schema validation in the request-validation plugin. For example, `{"string_payload":"bad","string_payload":"good"}` can be used to hide the "bad" input. Systems satisfy three conditions below are affected by this attack: 1. use body_schema validation in the request-validation plugin 2. upstream application uses a special JSON library that chooses the first occurred value, like jsoniter or gojay 3. upstream application does not validate the input anymore. The fix in APISIX is to re-encode the validated JSON input back into the request body at the side of APISIX. Improper Input Validation vulnerability in __COMPONENT__ of Apache APISIX allows an attacker to __IMPACT__. This issue affects Apache APISIX Apache APISIX version 2.12.1 and prior versions.

Severity

No CVSS data available.

CWE

CWE-20 - Improper Input Validation

Assigner

apache mitre root

References

2 references

URL	Tags
https://lists.apache.org/thread/03vd2j81krxmpz6xo…	x_refsource_MISC
http://www.openwall.com/lists/oss-security/2022/03/28/2	mailing-listx_refsource_MLIST

Impacted products

1 product

Vendor	Product	Version
Apache Software Foundation	Apache APISIX	Affected: Apache APISIX , ≤ 2.12.1 (custom)

Credits

Thanks for Guangli Dong from www.huoxian.cn

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.