8.8
Élevée
CVE-2026-54232 vLLM is an inference and serving engine for large language models (LLMs)
Description
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
Produits et versions touchés
| Produit | Versions touchées | Corrigé en |
|---|---|---|
| vllm 0.22 | premières → < 0.22.1 | 0.22.1 |
Références
- github.com github.com
Chronologie
- Publiée
- Dernière modification
Autres CVE de vllm
Les 121 CVE de vllm →
CVE-2026-115918
vLLM is an inference and serving engine for large language models
6.5
Moyenne
CVE-2026-115917
vLLM is an inference and serving engine for large language models
3.4
Faible
CVE-2026-115916
vLLM is an inference and serving engine for large language models
6.5
Moyenne
CVE-2026-115915
vLLM is an inference and serving engine for large language models
8.1
Élevée