Improper input validation in Vllm-Project Vllm
CVE-2026-105757
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine. This issue is fixed in version 0.30.0.
Vulnerability class: Drupalgeddon 2 (CVE-2018-7600)
Published · last modified .
CVSS v3 metric
CVSS v3 base score 6.5 (Medium). Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.
EPSS exploit prediction
No EPSS score from FIRST.org yet. New CVEs usually get one within a few days; no score means no signal yet, not low risk. How EPSS works.
Affected products
- Vllm-Project Vllm — versions < 0.30.0
Weakness classification (CWE)
References
Frequently asked questions
- What is CVE-2026-105757?
- CVE-2026-105757 is a medium-severity vulnerability in Vllm-Project Vllm, classified under Improper Input Validation. CVSS score: 6.5/10. Published 2026-10-05.
- How severe is CVE-2026-105757?
- Medium severity. CVSS v3 base score is 6.5 out of 10.