FFmpeg through 8.1.2, fixed in commit aafb5c6, contains a signed integer overflow vulnerability in the MACE6 audio decoder that allows attackers to corrupt heap memory by supplying a crafted CAF file with a malicious bytes_per_packet value. Attackers can craft a CAF file with oversized bytes_per_packet and frames_per_packet values in the desc chunk to trigger an integer overflow in mace_decode_frame() during output sample count computation, resulting in an undersized buffer allocation and heap out-of-bounds write that could enable code execution.
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
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Ffmpeg | Ffmpeg | * | 8.1.2 (including) |
| Red Hat Enterprise Linux AI 3.0 for RHEL 9 | RedHat | ffmpeg-0:6.1.6-9.el9ai | * |
| Red Hat Enterprise Linux AI 3.3 for RHEL 9 | RedHat | ffmpeg-0:6.1.6-7.el9ai | * |
| Red Hat Enterprise Linux AI 3.4 for RHEL 9 | RedHat | ffmpeg-0:6.1.6-7.el9ai | * |
| Red Hat Enterprise Linux AI 3.5 for RHEL 9 | RedHat | ffmpeg-0:6.1.6-7.el9ai | * |
| Red Hat Enterprise Linux AI 3.6 for RHEL 9 | RedHat | ffmpeg-0:6.1.6-8.el9ai | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/vllm-rocm-rhel9:1791425869 | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/vllm-gaudi-rhel9:1791427182 | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/model-opt-cuda-rhel9:1791803821 | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/vllm-cuda-rhel9:1791473631 | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/vllm-spyre-rhel9:1791818357 | * |
| Red Hat AI Inference Server 3.5 | RedHat | rhaii/vllm-cpu-rhel9:1791473614 | * |
| Red Hat OpenShift AI 2.25 | RedHat | rhoai/odh-vllm-gaudi-rhel9:1786712464 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-tpu-rhel9:1791231896 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-cuda-rhel9:1791232024 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-rocm-rhel9:1791231952 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-rocm-rhel9:1791231767 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-spyre-rhel9:1791232196 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-cpu-rhel9:1791232203 | * |
| Red Hat OpenShift AI 3.2 | RedHat | rhai/base-image-cuda-rhel9:1791818587 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-gaudi-rhel9:1790703552 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cpu-rhel9:1790703656 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-neuron-rhel9:1790703557 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-rubin-rhel9:1790703707 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cuda-13.0-rhel9:1790703645 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-tpu-rhel9:1790703554 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-rocm-7.14-rhel9:1790703554 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-spyre-rhel9:1790703630 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cuda-12.9-rhel9:1790703706 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cuda-13.0-rhel9:1791231778 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-spyre-rhel9:1791231888 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cpu-rhel9:1791232399 | * |
| Red Hat OpenShift AI 3.5 | RedHat | rhai/base-image-cuda-12.9-rhel9:1791231736 | * |
| Ffmpeg | Ubuntu | esm-apps-legacy/xenial | * |
| Ffmpeg | Ubuntu | esm-apps/bionic | * |
| Ffmpeg | Ubuntu | esm-apps/focal | * |
| Ffmpeg | Ubuntu | esm-apps/jammy | * |
| Ffmpeg | Ubuntu | esm-apps/noble | * |
| Ffmpeg | Ubuntu | esm-apps/resolute | * |
| Ffmpeg | Ubuntu | jammy | * |
| Ffmpeg | Ubuntu | noble | * |
| Ffmpeg | Ubuntu | resolute | * |
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].