CVE intelligence and bounded remediation

CVE-2026-44222 — vLLM is an inference and serving engine for large language models (LLMs)

High CVSS 7.5

vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.

Severity
High
CVSS
7.5 (3.1)
Published
2026-05-12
CISA KEV
Not currently listed
Ecosystem
software/application
Weaknesses
CWE-129

Affected products

  • vllm / vllm

Matched remediation archetype

Buffer bounds, memory safety, and memory corruption

This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.

Check exposure

  • Identify affected native-code versions, build flags, architectures, parsers, codecs, drivers, and input paths in all shipped artifacts.
  • Determine whether untrusted data reaches the affected routine and the process privilege, sandbox, and network exposure.
  • Confirm statically linked, vendored, firmware, and platform-provided copies, not only package-manager records.

Remediate safely

  • Apply the maintained upstream correction or replace the affected component, then rebuild every dependent artifact from clean inputs.
  • Adopt bounds-checked interfaces, validated sizes and integer conversions, clear ownership, and memory-safe components where practical.
  • Enable supported compiler and runtime hardening and add sanitized tests and fuzz regression seeds derived from non-weaponized fixtures.

Authoritative sources

Complete CVE record and remediation plan

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