Vulnerability-Lookup

GHSA-XQ47-RGWP-C6C5

Vulnerability from github – Published: 2025-09-05 18:31 – Updated: 2025-09-05 18:31

Details

NVIDIA Mellanox DPDK contains a vulnerability in Poll Mode Driver (PMD), where an attacker on a VM in the system might be able to cause information disclosure and denial of service on the network interface.

Severity

6.5 (Medium)


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

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{
  "affected": [],
  "aliases": [
    "CVE-2025-23259"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-362"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-09-04T16:15:34Z",
    "severity": "MODERATE"
  },
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  "id": "GHSA-xq47-rgwp-c6c5",
  "modified": "2025-09-05T18:31:14Z",
  "published": "2025-09-05T18:31:14Z",
  "references": [
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      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-23259"
    },
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      "type": "WEB",
      "url": "https://nvidia.custhelp.com/app/answers/detail/a_id/5655"
    },
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      "type": "WEB",
      "url": "https://www.cve.org/CVERecord?id=CVE-2025-23259"
    }
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  "severity": [
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      "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/A:H",
      "type": "CVSS_V3"
    }
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}

CVE-2025-23259 (GCVE-0-2025-23259)

Vulnerability from cvelistv5 – Published: 2025-09-04 15:52 – Updated: 2025-09-04 18:57

Summary

Severity

6.5 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-09-04 18:51 UTC

CWE

CWE-362 - Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')

Assigner

nvidia mitre root

References

3 references

URL	Tags
https://nvd.nist.gov/vuln/detail/CVE-2025-23259
https://www.cve.org/CVERecord?id=CVE-2025-23259
https://nvidia.custhelp.com/app/answers/detail/a_…

Impacted products

8 products

Vendor	Product	Version
NVIDIA	Mellanox DPDK 22.11	Affected: All versions prior to 22.11_2504.1.0
NVIDIA	Mellanox DPDK 22.11	Affected: All versions prior to 22.11_2410 LTS
NVIDIA	Mellanox DPDK 22.11	Affected: All versions prior to 22.11_2310 LTS
NVIDIA	Mellanox DPDK 20.11	Affected: All versions prior to 20.11_7.8.0 LTS
NVIDIA	Upstream DPDK	Affected: All versions prior to 25.07
NVIDIA	Upstream DPDK	Affected: All versions prior to 24.11.3 LTS
NVIDIA	Upstream DPDK	Affected: All versions prior to 23.11.5 LTS
NVIDIA	Upstream DPDK	Affected: All versions prior to 22.11.10 LTS

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.