CVE-2026-105760 in vLLM
Summary
by MITRE • 10/06/2026
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
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Analysis
by VulDB Data Team • 10/06/2026
The vulnerability identified in vLLM versions prior to 0.30.0 represents a significant resource exhaustion risk stemming from insufficient input validation within the request-level configuration parameters. Specifically, the media_io_kwargs field allows callers to specify backend-specific options for video processing, including fps and max_frames when utilizing the GLMGA video backend. The core technical flaw lies in the absence of strict upper bounds or work ceilings on these values. This oversight enables an attacker to craft a compact request containing minimal valid video data but with extremely high values for frame count and frames per second settings.
When such a maliciously crafted request is processed, the GLMGA backend constructs and deduplicates a pre-decode frame-index list based directly on the supplied parameters rather than the actual content of the video file. This mechanism allows an attacker to trigger disproportionate computational overhead relative to the size of the input data. The system allocates memory for this index list and consumes CPU cycles during its construction and processing, regardless of whether the underlying media is small or trivially valid. Because vLLM operates as a shared inference engine handling multiple concurrent requests, this behavior directly impacts the stability and performance of other workloads running on the same instance.
The operational impact of this vulnerability is primarily characterized by denial-of-service conditions through resource exhaustion. The disproportionate consumption of CPU time and memory in the shared media-loading executor can lead to thread pool saturation, increased latency for legitimate users, or complete unavailability of the inference service if resources are fully depleted. This aligns with CWE-400, which describes Uncontrolled Resource Consumption, as well as CWE-770, Allocation of Resources Without Limits or Throttling. From a threat modeling perspective using the MITRE ATT&CK framework, this vulnerability facilitates resource hijacking and can be leveraged for Denial of Service (T1499) by exhausting system resources through crafted API requests that exploit logic flaws in input handling rather than traditional buffer overflow techniques.
Mitigation strategies focus on enforcing strict validation limits at the application layer before processing begins. Upgrading to vLLM version 0.30.0 or later is the primary remediation, as this release addresses the issue by implementing appropriate ceilings for fps and max_frames parameters. In environments where immediate upgrading is not feasible, administrators should implement network-level rate limiting or API gateway rules that restrict the maximum allowable values for these specific fields in media_io_kwargs requests. Additionally, deploying resource quotas at the container or process level can help contain the blast radius of such attacks by capping the total CPU and memory consumption allowed per request or user session.