Executive Summary

This vulnerability exists in vLLM, an inference and serving engine for large language models. Due to integer truncation in the GGUF dequantize kernels, only part of the output tensor is processed, while the rest remains uninitialized and retains residual data from GPU memory.

In multi-tenant inference deployments, this means that leftover data from other users' inference requests can be exposed, leading to information disclosure.

The issue affects versions from 0.5.5 until 0.23.1rc0 and is fixed in version 0.23.1rc0.

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Impact Analysis

The vulnerability can lead to information disclosure in multi-tenant environments where multiple users share the same GPU resources.

Specifically, residual GPU memory from one user's inference request may be exposed to another user, potentially leaking sensitive or confidential data.

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Mitigation Strategies

To mitigate this vulnerability, upgrade vLLM to version 0.23.1rc0 or later, where the integer truncation issue in the GGUF dequantize kernels has been fixed.

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Compliance Impact

The vulnerability in vLLM causes partial tensor processing that may lead to residual GPU memory containing tensor data from other users' inference requests. In multi-tenant inference deployments, this residual data exposure constitutes information disclosure.

Such information disclosure can impact compliance with data protection regulations like GDPR and HIPAA, which require strict controls to prevent unauthorized access to personal or sensitive data.

Therefore, this vulnerability could lead to violations of these standards by exposing data from one user to another without authorization.

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