PYSEC-2026-227
See a problem?- Import Source
- https://github.com/pypa/advisory-database/blob/main/vulns/vllm/PYSEC-2026-227.yaml
- JSON Data
- https://api.osv.dev/v1/vulns/PYSEC-2026-227
- Aliases
-
- CVE-2026-54232
- GHSA-jrf6-vqxq-pjv2
- Published
- 2026-06-22T23:16:30.873Z
- Modified
- 2026-06-26T00:00:06.054142968Z
- Severity
-
- 8.8 (High) CVSS_V3 - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H CVSS Calculator
- Summary
- [none]
- Details
-
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 UVINDEXSTRATEGY="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.
- References
Affected packages
PyPI / vllm
Package
- Name
- vllm
- View open source insights on deps.dev
- Purl
- pkg:pypi/vllm
Affected ranges
- Type
- ECOSYSTEM
- Events
-
Introduced0Unknown introduced version / All previous versions are affectedFixed0.22.1
Affected versions
0.*
0.0.1
0.1.0
0.1.1
0.1.2
0.1.3
0.1.4
0.1.5
0.1.6
0.1.7
0.2.0
0.2.1
0.2.1.post1
0.2.2
0.2.3
0.2.4
0.2.5
0.2.6
0.2.7
0.3.0
0.3.1
0.3.2
0.3.3
0.4.0
0.4.0.post1
0.4.1
0.4.2
0.4.3
0.5.0
0.5.0.post1
0.5.1
0.5.2
0.5.3
0.5.3.post1
0.5.4
0.5.5
0.6.0
0.6.1
0.6.1.post1
0.6.1.post2
0.6.2
0.6.3
0.6.3.post1
0.6.4
0.6.4.post1
0.6.5
0.6.6
0.6.6.post1
0.7.0
0.7.1
0.7.2
0.7.3
0.8.0
0.8.1
0.8.2
0.8.3
0.8.4
0.8.5
0.8.5.post1
0.9.0
0.9.0.1
0.9.1
0.9.2
0.10.0
0.10.1
0.10.1.1
0.10.2
0.11.0
0.11.1
0.11.2
0.12.0
0.13.0
0.14.0
0.14.1
0.15.0
0.15.1
0.16.0
0.17.0
0.17.1
0.18.0
0.18.1
0.19.0
0.19.1
0.20.0
0.20.1
0.20.2
0.21.0
0.22.0