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CVE-2024-50288 (GCVE-0-2024-50288)

Vulnerability from cvelistv5 – Published: 2024-11-19 01:30 – Updated: 2026-08-05 11:43
VLAI
Title
media: vivid: fix buffer overwrite when using > 32 buffers
Summary
In the Linux kernel, the following vulnerability has been resolved: media: vivid: fix buffer overwrite when using > 32 buffers The maximum number of buffers that can be requested was increased to 64 for the video capture queue. But video capture used a must_blank array that was still sized for 32 (VIDEO_MAX_FRAME). This caused an out-of-bounds write when using buffer indices >= 32. Create a new define MAX_VID_CAP_BUFFERS that is used to access the must_blank array and set max_num_buffers for the video capture queue. This solves a crash reported by: https://bugzilla.kernel.org/show_bug.cgi?id=219258
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-01 20:14 UTC
CWE
Impacted products
Vendor Product Version CPE status
Linux Linux Affected: cea70ed416b428f8214be196d62cc7ffaa11f1b8 , < e6bacd8f2178b22859fe6d9f755f19dfcd9d3862 (git)
Affected: cea70ed416b428f8214be196d62cc7ffaa11f1b8 , < 96d8569563916fe2f8fe17317e20e43f54f9ba4b (git)
guessed Create a notification for this product.
Linux Linux Affected: 6.8
Unaffected: 0 , < 6.8 (semver)
Unaffected: 6.11.8 , ≤ 6.11.* (semver)
Unaffected: 6.12 , ≤ * (original_commit_for_fix)
guessed Create a notification for this product.
Show details on NVD website

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      "aggregate_severity": "Moderate",
      "current_release_date": "2026-08-04T14:17:03+00:00",
      "cve": "CVE-2024-50288",
      "id": "CVE-2024-50288",
      "initial_release_date": "2024-11-19T00:00:00+00:00",
      "product_status:known_not_affected": "198",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "kernel: media: vivid: fix buffer overwrite when using \u003e 32 buffers",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2024/cve-2024-50288.json",
      "version": "3"
    },
    "suse_vex": {
      "aggregate_severity": "important",
      "current_release_date": "2026-08-30T02:17:56Z",
      "cve": "CVE-2024-50288",
      "id": "CVE-2024-50288",
      "initial_release_date": "2024-11-19T03:49:07Z",
      "product_status:known_not_affected": "477",
      "source": "SUSE CSAF VEX",
      "status": "interim",
      "title": "SUSE CVE CVE-2024-50288",
      "url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2024-50288.json",
      "version": "23"
    },
    "vulnrichment": {
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      "cveMetadata": "{\"cveId\": \"CVE-2024-50288\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2025-10-01T20:17:21.514Z\", \"dateReserved\": \"2024-10-21T19:36:19.984Z\", \"assignerOrgId\": \"416baaa9-dc9f-4396-8d5f-8c081fb06d67\", \"datePublished\": \"2024-11-19T01:30:32.682Z\", \"assignerShortName\": \"Linux\"}",
      "dataType": "CVE_RECORD",
      "dataVersion": "5.1"
    }
  }
}



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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.

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Detection rules are retrieved from Rulezet.

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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.


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