CVE-2026-109311 in vLLM
Summary
by MITRE • 10/12/2026
vLLM before 0.29.0 passes uploaded audio to the PyAV/FFmpeg av.open() fallback with only a file-size check, exposing FFmpeg's IAMF parser heap overflow. Authenticated remote attackers can repeatedly upload malformed IAMF containers to /v1/audio/transcriptions, corrupting the heap and crashing the API process.
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Analysis
by VulDB Data Team • 10/12/2026
The vulnerability identified in vLLM versions prior to 0.29.0 represents a critical security flaw within its audio processing pipeline, specifically affecting the handling of uploaded media files for transcription services. The core issue stems from an insufficient input validation mechanism when passing user-uploaded audio data to the underlying PyAV library and its associated FFmpeg backend. When a client submits an audio file via the /v1/audio/transcriptions endpoint, vLLM performs only a superficial check on the file size before delegating the parsing responsibility to av.open(). This lack of deep content inspection allows malformed or maliciously crafted IAMF (Immersive Audio Metadata) containers to bypass initial security gates and reach the FFmpeg parser. The flaw is particularly severe because it exposes a heap overflow vulnerability within FFmpeg's IAMF parser, which can be triggered by repeatedly uploading specifically constructed malformed files.
From a technical perspective, this vulnerability falls under CWE-120: Buffer Copy without Checking Size of Input (Classic Buffer Overflow). By relying solely on file size constraints rather than validating the structural integrity and content boundaries of the audio container, vLLM permits data that exceeds expected buffer limits to be processed by FFmpeg. The IAMF parser in FFmpeg does not adequately verify bounds when processing certain metadata structures within these containers. Consequently, an authenticated attacker can craft a malicious IAMF file where specific fields contain oversized or malformed data. When this data is parsed, it causes a heap overflow, leading to memory corruption. This type of exploitation leverages the trust placed in the input stream by the application layer, exploiting the gap between high-level API handling and low-level media parsing libraries.
The operational impact of this vulnerability is significant for any deployment utilizing vLLM's audio transcription capabilities with authenticated access enabled. An attacker who has valid credentials can repeatedly send these malformed IAMF containers to trigger the heap corruption consistently. The immediate result is a denial of service, as the memory corruption typically causes the API process to crash or become unstable. However, beyond simple availability disruption, heap overflows in complex parsers like FFmpeg often present opportunities for arbitrary code execution if an attacker can precisely control the overwritten memory regions and exploit specific allocator behaviors. This transforms what might appear as a stability issue into a potential remote code execution vector, compromising the integrity of the underlying system hosting the vLLM instance.
In terms of threat modeling, this vulnerability aligns with MITRE ATT&CK technique T1496: Resource Hijacking, specifically through denial-of-service impacts caused by resource exhaustion or process crashes. It also relates to CWE-20: Improper Input Validation, as the application fails to restrict or verify inputs that could lead to adverse effects on system operation. The attack vector is remote and requires authentication, which limits its scope compared to unauthenticated exploits but still poses a serious risk in environments where internal users or services have access to the transcription API.
To mitigate this vulnerability, organizations running vLLM must upgrade immediately to version 0.29.0 or later, where the input validation logic has been strengthened to properly sanitize and validate audio files before they are passed to the FFmpeg backend. In addition to upgrading, administrators should implement strict rate limiting on the /v1/audio/transcriptions endpoint to prevent rapid-fire uploads that could facilitate repeated exploitation attempts. Furthermore, deploying web application firewalls or custom middleware to inspect uploaded media files for known malicious patterns in IAMF structures can provide an additional layer of defense. It is also advisable to run vLLM processes with minimal privileges and within isolated containers to limit the blast radius should a heap overflow lead to unexpected behavior or process termination. Regularly updating FFmpeg dependencies, as they are often targeted by security researchers for parser vulnerabilities, remains a best practice in maintaining robust media processing pipelines.