Vulnerability-Lookup

GHSA-FJ4X-VQ3P-RJCF

Vulnerability from github – Published: 2025-12-10 21:31 – Updated: 2025-12-11 21:31

Details

The mobile application insecurely handles information stored within memory. By performing a memory dump on the application after a user has logged out and terminated it, Wi-Fi credentials sent during the pairing process, JWTs used for authentication, and other sensitive details can be retrieved. As a result, an attacker with physical access to the device of a victim can retrieve this information and gain unauthorized access to their home Wi-Fi network and Meatmeet account.

Severity

4.6 (Medium)


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

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{
  "affected": [],
  "aliases": [
    "CVE-2025-65832"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-316"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-12-10T21:16:09Z",
    "severity": "MODERATE"
  },
  "details": "The mobile application insecurely handles information stored within memory. By performing a memory dump on the application after a user has logged out and terminated it, Wi-Fi credentials sent during the pairing process, JWTs used for authentication, and other sensitive details can be retrieved. As a result, an attacker with physical access to the device of a victim can retrieve this information and gain unauthorized access to their home Wi-Fi network and Meatmeet account.",
  "id": "GHSA-fj4x-vq3p-rjcf",
  "modified": "2025-12-11T21:31:27Z",
  "published": "2025-12-10T21:31:38Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-65832"
    },
    {
      "type": "WEB",
      "url": "https://gist.github.com/dead1nfluence/4dffc239b4a460f41a03345fd8e5feb5#file-sensitive-information-stored-in-memory-md"
    },
    {
      "type": "WEB",
      "url": "https://github.com/dead1nfluence/Meatmeet-Pro-Vulnerabilities/blob/main/Mobile-Application/Sensitive%20Information-Stored-in-Memory.md"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:P/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ]
}

CVE-2025-65832 (GCVE-0-2025-65832)

Vulnerability from cvelistv5 – Published: 2025-12-10 00:00 – Updated: 2025-12-11 20:35

Summary

Severity

4.6 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-12-11 20:02 UTC

CWE

n/a
CWE-316 - Cleartext Storage of Sensitive Information in Memory

Assigner

mitre mitre root

References

2 references

URL	Tags
https://github.com/dead1nfluence/Meatmeet-Pro-Vul…
https://gist.github.com/dead1nfluence/4dffc239b4a…

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{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "cvssV3_1": {
              "attackComplexity": "LOW",
              "attackVector": "PHYSICAL",
              "availabilityImpact": "NONE",
              "baseScore": 4.6,
              "baseSeverity": "MEDIUM",
              "confidentialityImpact": "HIGH",
              "integrityImpact": "NONE",
              "privilegesRequired": "NONE",
              "scope": "UNCHANGED",
              "userInteraction": "NONE",
              "vectorString": "CVSS:3.1/AV:P/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
              "version": "3.1"
            }
          },
          {
            "other": {
              "content": {
                "id": "CVE-2025-65832",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-12-11T20:02:45.425042Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "problemTypes": [
          {
            "descriptions": [
              {
                "cweId": "CWE-316",
                "description": "CWE-316 Cleartext Storage of Sensitive Information in Memory",
                "lang": "en",
                "type": "CWE"
              }
            ]
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-12-11T20:35:11.370Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "n/a",
          "vendor": "n/a",
          "versions": [
            {
              "status": "affected",
              "version": "n/a"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "The mobile application insecurely handles information stored within memory. By performing a memory dump on the application after a user has logged out and terminated it, Wi-Fi credentials sent during the pairing process, JWTs used for authentication, and other sensitive details can be retrieved. As a result, an attacker with physical access to the device of a victim can retrieve this information and gain unauthorized access to their home Wi-Fi network and Meatmeet account."
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "description": "n/a",
              "lang": "en",
              "type": "text"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2025-12-10T20:44:10.811Z",
        "orgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
        "shortName": "mitre"
      },
      "references": [
        {
          "url": "https://github.com/dead1nfluence/Meatmeet-Pro-Vulnerabilities/blob/main/Mobile-Application/Sensitive%20Information-Stored-in-Memory.md"
        },
        {
          "url": "https://gist.github.com/dead1nfluence/4dffc239b4a460f41a03345fd8e5feb5#file-sensitive-information-stored-in-memory-md"
        }
      ]
    }
  },
  "cveMetadata": {
    "assignerOrgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
    "assignerShortName": "mitre",
    "cveId": "CVE-2025-65832",
    "datePublished": "2025-12-10T00:00:00.000Z",
    "dateReserved": "2025-11-18T00:00:00.000Z",
    "dateUpdated": "2025-12-11T20:35:11.370Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

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.