CVE-2026-54232
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.
- CVSS base score ≥ 7.0
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HATT&CK techniques
6Techniques this CVE enables. Pills with a solid outline are high confidence - named directly in ATT&CK or Nuclei, or human-curated by CTID; the rest are inferred from the weakness type using MITRE's CVE Mapping Methodology and the CWE → CAPEC chain. Broad, generic-weakness guesses are filtered out. A small N× marks a technique that N independent sources agree on.
CAPEC attack patterns
2Attack patterns this CVE enables - the bridge from weakness to ATT&CK technique.