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CVE-2026-54232 (GCVE-0-2026-54232)
Vulnerability from cvelistv5 – Published: 2026-06-22 22:16 – Updated: 2026-06-23 14:30
VLAI
Title
vLLM: Dependency Confusion Vulnerability in vLLM Dockerfile
Summary
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)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
- CWE-427 - Uncontrolled Search Path Element
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.22.1
|
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"id": "CVE-2026-54232",
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"timestamp": "2026-06-23T14:29:54.064861Z",
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"value": "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."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
}
}
],
"problemTypes": [
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"descriptions": [
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"references": [
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"tags": [
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"source": {
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"discovery": "UNKNOWN"
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"title": "vLLM: Dependency Confusion Vulnerability in vLLM Dockerfile"
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"datePublished": "2026-06-22T22:16:43.101Z",
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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"
}
]
}