CVE Vulnerabilities

CVE-2026-64830

Heap-based Buffer Overflow

Published: Jul 22, 2026 | Modified: Jul 28, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
8.8 IMPORTANT
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Ubuntu
MEDIUM
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FFmpeg versions 2.1 through 8.1.2 contains a heap buffer overflow vulnerability in the VobSub subtitle demuxer that allows attackers to corrupt adjacent heap memory by supplying a malicious .sub/.idx subtitle file declaring more distinct stream IDs than the fixed-size array bounds in libavformat/mpeg.c. Attackers can craft a subtitle file with excessive distinct stream IDs to trigger unbounded writes beyond the vobsub->q[] array boundary via ff_subtitles_queue_insert(), potentially achieving arbitrary code execution in any application using FFmpegs VobSub demuxer.

Weakness

A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().

Affected Software

NameVendorStart VersionEnd Version
FfmpegFfmpeg2.1 (including)8.1.2 (including)
Red Hat Enterprise Linux AI 3.0 for RHEL 9RedHatffmpeg-0:6.1.6-9.el9ai*
Red Hat Enterprise Linux AI 3.3 for RHEL 9RedHatffmpeg-0:6.1.6-9.el9ai*
Red Hat Enterprise Linux AI 3.4 for RHEL 9RedHatffmpeg-0:6.1.6-9.el9ai*
Red Hat Enterprise Linux AI 3.5 for RHEL 9RedHatffmpeg-0:6.1.6-9.el9ai*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:1787860580*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:1787772157*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:1787884873*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-tpu-rhel9:1791231896*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-cuda-rhel9:1791232024*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-rocm-rhel9:1791231952*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-rocm-rhel9:1791231767*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-spyre-rhel9:1791232196*
Red Hat OpenShift AI 3.2RedHatrhai/base-image-cpu-rhel9:1791232203*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cpu-rhel9:1790703615*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-tpu-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-6.4-rhel9:1790703524*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-13.0-rhel9:1790703601*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-7.0-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-12.9-rhel9:1790703608*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-spyre-rhel9:1790703579*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-neuron-rhel9:1790703516*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cpu-rhel9:1791232133*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-13.0-rhel9:1791231820*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-12.9-rhel9:1791232257*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-neuron-rhel9:1790703542*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-tpu-rhel9:1790703539*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-gaudi-rhel9:1790703553*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cpu-rhel9:1790703631*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-spyre-rhel9:1790703597*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-13.0-rhel9:1790703641*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-12.9-rhel9:1790703630*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-7.1-rhel9:1790703541*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-6.4-rhel9:1790703545*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-gaudi-rhel9:1790703552*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cpu-rhel9:1790703656*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-neuron-rhel9:1790703557*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rubin-rhel9:1790703707*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-13.0-rhel9:1790703645*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-tpu-rhel9:1790703554*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rocm-7.14-rhel9:1790703554*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-spyre-rhel9:1790703630*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-12.9-rhel9:1790703706*
FfmpegUbuntudevel*
FfmpegUbuntuesm-apps-legacy/xenial*
FfmpegUbuntuesm-apps/bionic*
FfmpegUbuntuesm-apps/focal*
FfmpegUbuntuesm-apps/jammy*
FfmpegUbuntuesm-apps/noble*
FfmpegUbuntuesm-apps/resolute*
FfmpegUbuntujammy*
FfmpegUbuntunoble*
FfmpegUbunturesolute*
FfmpegUbuntuupstream*

Potential Mitigations

  • Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
  • D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
  • Run or compile the software using features or extensions that randomly arrange the positions of a program’s executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
  • Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as “rebasing” (for Windows) and “prelinking” (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
  • For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].

References