GCD AI RADAR
incidents/AIR-2026-1122

CVE-2026-54232: Dependency confusion flaw in vLLM Dockerfile

Attack on AI2 Disclosed vulnerabilityInfrastructure vulnerabilitySupply chain

A dependency confusion vulnerability in vLLM prior to version 0.22.1 allows attackers to execute arbitrary code during Docker image builds. Registration of a malicious package name on PyPI enables container image backdooring.

disclosed CVENo confirmed exploitation, so this record is tracked as a disclosed vulnerability and not counted as an incident. It moves into the counts if CISA or the vendor confirms exploitation. Why

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Infrastructure vulnerabilityprimary
    CVEs and advisories in AI frameworks, inference servers, MCP servers, AI developer tools and hosting stacks.
  • Supply chain
    Malicious or backdoored models, poisoned packages, unsafe deserialization and other compromised AI dependencies.
owasp llm top 10
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
2 · Disclosed vulnerability
CVE or vendor advisory, patched or patchable, no confirmed exploitation.
targets
Framework / tooling
products
vLLM
incident date
22 Jun 2026
first reported
22 Jun 2026
origin
NVD
Vulnerability record from NVD / the GitHub Advisory Database. This product uses the NVD API but is not endorsed or certified by the NVD.
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
CVE-2026-54232 · dependency_confusion · supply_chain
record
created 2026-10-18 10:04Z · updated 2026-10-18 10:04Z · json

Sources · 1

  1. 2026-06-22
    CVE-2026-54232: vLLM is an inference and serving engine for large language models (LLMs).
    NVD · nvd.nist.gov/vuln/detail/CVE-2026-54232

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