PYSEC-2026-227
Vulnerability from pysec - Published: 2026-06-22 23:16 - Updated: 2026-06-25 23:11
VLAI
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 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.
Severity
8.8 (High)
Impacted products
| Name | purl | vllm | pkg:pypi/vllm |
|---|
Aliases
{
"affected": [
{
"ecosystem_specific": {},
"package": {
"ecosystem": "PyPI",
"name": "vllm",
"purl": "pkg:pypi/vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.22.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"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",
"0.10.0",
"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",
"0.2.0",
"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",
"0.20.0",
"0.20.1",
"0.20.2",
"0.21.0",
"0.22.0",
"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"
]
}
],
"aliases": [
"CVE-2026-54232",
"GHSA-jrf6-vqxq-pjv2"
],
"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 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.",
"id": "PYSEC-2026-227",
"modified": "2026-06-25T23:11:27.474744Z",
"published": "2026-06-22T23:16:30.873Z",
"references": [
{
"type": "EVIDENCE",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-jrf6-vqxq-pjv2"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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