CVE-2026-8461 (GCVE-0-2026-8461)

Vulnerability from cvelistv5 – Published: 2026-06-18 11:29 – Updated: 2026-07-23 12:08
VLAI
Title
Heap out-of-bounds write via odd slice_height in FFmpeg MagicYUV decoder
Summary
An out-of-bounds write vulnerability in FFmpeg's libavcodec library, specifically in the MagicYUV decoder, allows denial-of-service and, in some cases, can be exploited for remote code execution. This vulnerability is associated with the file libavcodec/magicyuv.C. This issue affects FFmpeg before version 8.1.2.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 00:00 UTC
CWE
Assigner
JFROG CNA under the mitre root
CNA scorecard B 76/100 over 66 records in the last 180 days details
Impacted products
Vendor Product Version CPE status
FFmpeg FFmpeg Affected: 0 , < 8.1.2 (custom)
guessed Create a notification for this product.
Red Hat Red Hat Enterprise Linux AI 3.5 for RHEL 9 Unaffected: 0:6.1.6-1.el9ai , < * (rpm)
    cpe:/a:redhat:enterprise_linux_ai:3.5::el9
Create a notification for this product.
Red Hat Red Hat AI Inference Server     cpe:/a:redhat:ai_inference_server:3
Create a notification for this product.
Red Hat Red Hat Enterprise Linux AI (RHEL AI) 3     cpe:/a:redhat:enterprise_linux_ai:3
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Red Hat Red Hat OpenShift AI (RHOAI)     cpe:/a:redhat:openshift_ai
Create a notification for this product.
Date Public
2026-06-18 11:14
Show details on NVD website

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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

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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
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  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
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Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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How durations are calculated

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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.