CVE-2026-115913 in vLLM
Summary
by MITRE • 10/26/2026
vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the Phi-4 multimodal audio encoder in phi4mm_audio.py and phi4mm_utils.py uses eager attention by default, allowing a remote client to submit one legal long audio clip that causes MultiHeadedAttention.forward_attention to materialize and retain full attention matrices in self.attn. The scaled dot-product attention branch does not materialize these matrices. The resulting quadratic GPU-memory use can cause an out-of-memory condition and terminate EngineCore for all users. This issue is fixed in version 0.32.0.
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Analysis
by VulDB Data Team • 10/26/2026
The vulnerability identified in vLLM, specifically within versions prior to 0.32.0, represents a significant resource exhaustion risk stemming from the implementation of attention mechanisms in large language model serving engines. vLLM is widely deployed as an inference and serving engine for various large language models, including multimodal variants such as Phi-4. The core issue resides in the audio encoder components, specifically within the files phi4mm_audio.py and phi4mm_utils.py, where the default configuration utilizes eager attention rather than optimized alternatives like flash attention or memory-efficient implementations. This architectural choice creates a scenario where computational resources are allocated inefficiently during the processing of input data, particularly when handling long sequences.
From a technical perspective, the flaw is located in the MultiHeadedAttention.forward_attention method. When this function processes audio inputs using eager attention, it materializes and retains full attention matrices within the self.attn attribute for every forward pass. In contrast, other branches such as scaled dot-product attention do not exhibit this behavior because they utilize algorithms that compute outputs without storing the intermediate dense matrix of all pairwise interactions between tokens. The retention of these full attention matrices results in quadratic GPU memory consumption relative to the sequence length. This means that as the input audio clip grows longer, the memory required does not increase linearly but rather squares, leading to rapid and substantial resource depletion on the underlying hardware infrastructure.
The operational impact of this vulnerability is severe for multi-tenant environments or shared inference services. A remote client can exploit this behavior by submitting a single legal long audio clip that triggers the quadratic memory allocation pattern. This action causes an out-of-memory condition within the GPU, which subsequently terminates the EngineCore process. Since vLLM typically serves multiple users concurrently through a unified engine instance, the termination of EngineCore disrupts service availability for all connected clients, not just the attacker. This constitutes a denial-of-service scenario where a single request can degrade or completely halt the functionality of the entire inference server, affecting business continuity and user experience across the platform.
This vulnerability aligns with CWE-400, which describes Uncontrolled Resource Consumption, as the application fails to limit the amount of resources consumed by an individual input. Furthermore, it maps to MITRE ATT&CK technique T1498, Network Denial of Service, specifically under sub-techniques involving resource exhaustion through volumetric or computational means. The attack vector is remote and requires no authentication if the inference endpoint is publicly accessible, making it a high-severity risk for any production deployment exposing vLLM endpoints to external traffic without adequate input size restrictions or rate limiting.
Mitigation strategies primarily involve upgrading to version 0.32.0 of vLLM, where this issue has been resolved by addressing the attention mechanism implementation in the affected modules. For environments that cannot immediately upgrade, administrators should implement strict limits on audio clip length and duration at the API gateway level before requests reach the inference engine. Additionally, configuring resource quotas or container-level memory constraints can help contain the impact of such attacks, preventing a single process from consuming all available GPU memory and crashing the host system. Monitoring for unusual spikes in GPU memory usage associated with specific endpoints can also aid in early detection of exploitation attempts.