GHSA-78PW-6G8H-CP3C

Vulnerability from github – Published: 2024-11-28 18:38 – Updated: 2024-11-28 18:38
VLAI
Details

The Wallet for WooCommerce plugin for WordPress is vulnerable to incorrect conversion between numeric types in all versions up to, and including, 1.5.6. This is due to a numerical logic flaw when transferring funds to another user. This makes it possible for authenticated attackers, with Subscriber-level access and above, to create funds during a transfer and distribute these funds to any number of other users or their own account, rendering products free. Attackers could also request to withdraw funds if the Wallet Withdrawal extension is used and the request is approved by an administrator.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2024-7747"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-681"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-11-28T13:15:21Z",
    "severity": "MODERATE"
  },
  "details": "The Wallet for WooCommerce plugin for WordPress is vulnerable to incorrect conversion between numeric types in all versions up to, and including, 1.5.6. This is due to a numerical logic flaw when transferring funds to another user. This makes it possible for authenticated attackers, with Subscriber-level access and above, to create funds during a transfer and distribute these funds to any number of other users or their own account, rendering products free. Attackers could also request to withdraw funds if the Wallet Withdrawal extension is used and the request is approved by an administrator.",
  "id": "GHSA-78pw-6g8h-cp3c",
  "modified": "2024-11-28T18:38:37Z",
  "published": "2024-11-28T18:38:37Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-7747"
    },
    {
      "type": "WEB",
      "url": "https://plugins.trac.wordpress.org/browser/woo-wallet/trunk/includes/class-woo-wallet-frontend.php#L407"
    },
    {
      "type": "WEB",
      "url": "https://plugins.trac.wordpress.org/changeset/3145131"
    },
    {
      "type": "WEB",
      "url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/fd8f3eb7-ac60-46c4-b41f-5d89e3133042?source=cve"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N",
      "type": "CVSS_V3"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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

Sightings and recorded dates, grouped by day · UTC

JSON
Events included in the timeline

Loading timeline…


    Loading…

    Loading…

    Loading…

    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.