Vulnerability-Lookup

CNVD-2025-12634

Vulnerability from cnvd - Published: 2025-06-16

Title

Mattermost代码问题漏洞

Description

Mattermost是美国Mattermost公司的一个开源协作平台。 Mattermost存在代码问题漏洞，该漏洞源于未能正确验证提供给操作样式的proto样式，攻击者可利用该漏洞通过精心设计的恶意输入使前端崩溃。

Severity

中

Patch Name

Mattermost代码问题漏洞的补丁

Patch Description

Mattermost是美国Mattermost公司的一个开源协作平台。 Mattermost存在代码问题漏洞，该漏洞源于未能正确验证提供给操作样式的proto样式，攻击者可利用该漏洞通过精心设计的恶意输入使前端崩溃。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

Formal description

目前厂商已发布升级程序修复该安全问题，详情见厂商官网： https://mattermost.com/security-updates

Reference

https://nvd.nist.gov/vuln/detail/CVE-2025-21088

Impacted products

Name
['Mattermost Mattermost >=10.2.x，<=10.2.0', 'Mattermost Mattermost >=9.11.x，<=9.11.5', 'Mattermost Mattermost >=10.0.x，<=10.0.3', 'Mattermost Mattermost >=10.1.x，<=10.1.3']

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JSON

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{
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    "cve": {
      "cveNumber": "CVE-2025-21088",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-21088"
    }
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  "number": "CNVD-2025-12634",
  "openTime": "2025-06-16",
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  "products": {
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      "Mattermost Mattermost \u003e=9.11.x\uff0c\u003c=9.11.5",
      "Mattermost Mattermost \u003e=10.0.x\uff0c\u003c=10.0.3",
      "Mattermost Mattermost \u003e=10.1.x\uff0c\u003c=10.1.3"
    ]
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  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2025-21088",
  "serverity": "\u4e2d",
  "submitTime": "2025-02-08",
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}

CVE-2025-21088 (GCVE-0-2025-21088)

Vulnerability from cvelistv5 – Published: 2025-01-15 15:51 – Updated: 2025-01-15 16:20

Title

WebApp crash via improper validation of proto style in attachments

Summary

Mattermost versions 10.2.x <= 10.2.0, 9.11.x <= 9.11.5, 10.0.x <= 10.0.3, 10.1.x <= 10.1.3 fail to properly validate the style of proto supplied to an action's style in post.props.attachments, which allows an attacker to crash the frontend via crafted malicious input.

Severity

6.5 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-01-15 16:19 UTC

CWE

CWE-704 - Incorrect Type Conversion or Cast

Assigner

Mattermost mitre root

References

1 reference

URL	Tags
https://mattermost.com/security-updates

Impacted products

1 product

Vendor	Product	Version
Mattermost	Mattermost	Affected: 10.2.0 Affected: 9.11.0 , ≤ 9.11.5 (semver) Affected: 10.0.0 , ≤ 10.0.3 (semver) Affected: 10.1.0 , ≤ 10.1.3 (semver) Unaffected: 10.3.0 Unaffected: 10.2.1 Unaffected: 9.11.6 Unaffected: 10.0.4 Unaffected: 10.1.4

Credits

c0rydoras

Show details on NVD website

JSON

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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.