CVE-2026-54232vLLM is an inference and serving engine for large language models (LLMs).
Updated under an hour ago ()
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No exploitation reported and no EPSS estimate yet.
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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
- 8.8 HIGH (v3.1, NVD)CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
- Weakness (CWE)
- CWE-427
- Known exploited
- Not in CISA's KEV catalog
- EPSS (exploitation in the next 30 days)
- Not loaded for this CVE
- Assigned by
- Not given
- NVD analysis
- Analyzed
- Record last updated
- Jun 24, 2026
- First seen by InfoSec Signals
- Oct 9, 2026
Affected products
vllm vllm vllm:vllm | before 0.22.1 | NVD |
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-jrf6-vqxq-pjv2exploit, third-party-advisory
The CVE record on cve.org NVD's entry
The CVE record is authoritative. Products marked “from the name” were identified from the CNA's free-text names, not a CPE, and are matched less certainly. This product uses the NVD API but is not endorsed or certified by the NVD.