Vulnerability-Lookup

GHSA-C55F-PX99-57C7

Vulnerability from github – Published: 2022-05-24 17:22 – Updated: 2022-05-24 17:22

Details

MX Player Android App versions prior to v1.24.5, are vulnerable to a directory traversal vulnerability when user is using the MX Transfer feature in "Receive" mode. An attacker can exploit this by connecting to the MX Transfer session as a "sender" and sending a MessageType of "FILE_LIST" with a "name" field containing directory traversal characters (../). This will result in the file being transferred to the victim's phone, but being saved outside of the intended "/sdcard/MXshare" directory. In some instances, an attacker can achieve remote code execution by writing ".odex" and ".vdex" files in the "oat" directory of the MX Player application.

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{
  "affected": [],
  "aliases": [
    "CVE-2020-5764"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2020-07-08T14:15:00Z",
    "severity": "MODERATE"
  },
  "details": "MX Player Android App versions prior to v1.24.5, are vulnerable to a directory traversal vulnerability when user is using the MX Transfer feature in \"Receive\" mode. An attacker can exploit this by connecting to the MX Transfer session as a \"sender\" and sending a MessageType of \"FILE_LIST\" with a \"name\" field containing directory traversal characters (../). This will result in the file being transferred to the victim\u0027s phone, but being saved outside of the intended \"/sdcard/MXshare\" directory. In some instances, an attacker can achieve remote code execution by writing \".odex\" and \".vdex\" files in the \"oat\" directory of the MX Player application.",
  "id": "GHSA-c55f-px99-57c7",
  "modified": "2022-05-24T17:22:36Z",
  "published": "2022-05-24T17:22:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-5764"
    },
    {
      "type": "WEB",
      "url": "https://www.tenable.com/security/research/tra-2020-41"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

CVE-2020-5764 (GCVE-0-2020-5764)

Vulnerability from cvelistv5 – Published: 2020-07-08 13:34 – Updated: 2024-08-04 08:39

Summary

Severity

No CVSS data available.

CWE

Path Traversal

Assigner

tenable CNA under the mitre root

CNA scorecard C 71/100 over 52 records in the last 180 days details

References

1 reference

URL	Tags
https://www.tenable.com/security/research/tra-2020-41	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
n/a	MX Player Android App	Affected: All versions prior to version v1.24.5

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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.

Vulnerability timeline

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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.

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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.

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