Vulnerability-Lookup

CNVD-2025-20001

Vulnerability from cnvd - Published: 2025-09-02

Title

NVIDIA Triton Inference Server拒绝服务漏洞

Description

NVIDIA Triton Inference Server是美国英伟达（NVIDIA）公司的一款开源软件，有助于标准化模型部署并在生产中提供快速且可扩展的AI。 NVIDIA Triton Inference Server存在拒绝服务漏洞，攻击者可利用该漏洞导致分段错误和拒绝服务。

Severity

高

Patch Name

NVIDIA Triton Inference Server拒绝服务漏洞的补丁

Patch Description

NVIDIA Triton Inference Server是美国英伟达（NVIDIA）公司的一款开源软件，有助于标准化模型部署并在生产中提供快速且可扩展的AI。 NVIDIA Triton Inference Server存在拒绝服务漏洞，攻击者可利用该漏洞导致分段错误和拒绝服务。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

厂商已发布了漏洞修复程序，请及时关注更新： https://nvidia.custhelp.com/app/answers/detail/a_id/5687

Reference

https://nvd.nist.gov/vuln/detail/CVE-2025-23331

Impacted products

Name
NVIDIA Triton Inference Server <25.06

Show details on source website

JSON

To clipboard

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  "cves": {
    "cve": {
      "cveNumber": "CVE-2025-23331",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-23331"
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  "number": "CNVD-2025-20001",
  "openTime": "2025-09-02",
  "patchDescription": "NVIDIA Triton Inference Server\u662f\u7f8e\u56fd\u82f1\u4f1f\u8fbe\uff08NVIDIA\uff09\u516c\u53f8\u7684\u4e00\u6b3e\u5f00\u6e90\u8f6f\u4ef6\uff0c\u6709\u52a9\u4e8e\u6807\u51c6\u5316\u6a21\u578b\u90e8\u7f72\u5e76\u5728\u751f\u4ea7\u4e2d\u63d0\u4f9b\u5feb\u901f\u4e14\u53ef\u6269\u5c55\u7684AI\u3002\r\n\r\nNVIDIA Triton Inference Server\u5b58\u5728\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u5206\u6bb5\u9519\u8bef\u548c\u62d2\u7edd\u670d\u52a1\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "NVIDIA Triton Inference Server\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "NVIDIA Triton Inference Server \u003c25.06"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2025-23331",
  "serverity": "\u9ad8",
  "submitTime": "2025-08-18",
  "title": "NVIDIA Triton Inference Server\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e"
}

CVE-2025-23331 (GCVE-0-2025-23331)

Vulnerability from cvelistv5 – Published: 2025-08-06 12:42 – Updated: 2025-08-06 13:46

Summary

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a memory allocation with excessive size value, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.

Severity

7.5 (High)


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

SSVC

Exploitation: none Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-08-06 13:45 UTC

CWE

CWE-789 - Memory Allocation with Excessive Size Value

Assigner

nvidia mitre root

References

3 references

URL	Tags
https://nvd.nist.gov/vuln/detail/CVE-2025-23331
https://www.cve.org/CVERecord?id=CVE-2025-23331
https://nvidia.custhelp.com/app/answers/detail/a_…

Impacted products

1 product

Vendor	Product	Version
NVIDIA	Triton Inference Server	Affected: All versions prior to 25.06

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.