Vulnerability-Lookup

CNVD-2025-19539

Vulnerability from cnvd - Published: 2025-08-21

Title

NVIDIA Megatron-LM代码注入漏洞

Description

NVIDIA Megatron-LM是美国英伟达（NVIDIA）公司的一个基于PyTorch的分布式训练框架，专门用于训练大型Transformer语言模型。 NVIDIA Megatron-LM存在代码注入漏洞，该漏洞源于工具组件，攻击者可利用该漏洞通过生成非法的代码段，修改网络系统或组件的执行控制流。

Severity

中

Patch Name

NVIDIA Megatron-LM代码注入漏洞的补丁

Patch Description

Formal description

厂商已提供漏洞升级程序，请及时关注更新： https://www.cve.org/CVERecord?id=CVE-2025-23305

Reference

https://nvd.nist.gov/vuln/detail/CVE-2025-23305

Impacted products

Name
NVIDIA Megatron-LM

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2025-23305",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-23305"
    }
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  "number": "CNVD-2025-19539",
  "openTime": "2025-08-21",
  "patchDescription": "NVIDIA Megatron-LM\u662f\u7f8e\u56fd\u82f1\u4f1f\u8fbe\uff08NVIDIA\uff09\u516c\u53f8\u7684\u4e00\u4e2a\u57fa\u4e8ePyTorch\u7684\u5206\u5e03\u5f0f\u8bad\u7ec3\u6846\u67b6\uff0c\u4e13\u95e8\u7528\u4e8e\u8bad\u7ec3\u5927\u578bTransformer\u8bed\u8a00\u6a21\u578b\u3002\r\n\r\nNVIDIA Megatron-LM\u5b58\u5728\u4ee3\u7801\u6ce8\u5165\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5de5\u5177\u7ec4\u4ef6\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u901a\u8fc7\u751f\u6210\u975e\u6cd5\u7684\u4ee3\u7801\u6bb5\uff0c\u4fee\u6539\u7f51\u7edc\u7cfb\u7edf\u6216\u7ec4\u4ef6\u7684\u6267\u884c\u63a7\u5236\u6d41\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 Megatron-LM\u4ee3\u7801\u6ce8\u5165\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "NVIDIA Megatron-LM"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2025-23305",
  "serverity": "\u4e2d",
  "submitTime": "2025-08-21",
  "title": "NVIDIA Megatron-LM\u4ee3\u7801\u6ce8\u5165\u6f0f\u6d1e"
}

CVE-2025-23305 (GCVE-0-2025-23305)

Vulnerability from cvelistv5 – Published: 2025-08-13 17:35 – Updated: 2025-08-13 18:02

Summary

NVIDIA Megatron-LM for all platforms contains a vulnerability in the tools component, where an attacker may exploit a code injection issue. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

Severity

7.8 (High)


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

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-08-13 18:02 UTC

CWE

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

Assigner

nvidia mitre root

References

3 references

URL	Tags
https://nvd.nist.gov/vuln/detail/CVE-2025-23305
https://www.cve.org/CVERecord?id=CVE-2025-23305
https://nvidia.custhelp.com/app/answers/detail/a_…

Impacted products

1 product

Vendor	Product	Version
NVIDIA	Megatron-LM	Affected: All versions prior to 0.12.2

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

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