vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
| Métrique | Valeur | Interprétation |
|---|---|---|
|
AV (Vecteur d'Attaque)
Attack Vector
|
Network (N)
|
Exploit possible à distance via réseau (HTTP, API, etc.) |
|
AC (Complexité d'Attaque)
Attack Complexity
|
Low (L)
|
Peu de conditions requises pour exploiter |
|
PR (Privilèges Requis)
Privileges Required
|
None (N)
|
Aucune authentification requise |
|
UI (Interaction Utilisateur)
User Interaction
|
Unknown (P)
|
Interaction inconnue |
|
S (Périmètre)
Scope
|
Unchanged (U)
|
Impact reste dans le périmètre du composant compromis |
|
C (Impact Confidentialité)
Confidentiality Impact
|
Low (L)
|
Divulgation limitée d'informations |
|
I (Impact Intégrité)
Integrity Impact
|
Low (L)
|
Modification limitée des données possible |
|
A (Impact Disponibilité)
Availability Impact
|
None (N)
|
Aucune interruption de service |