CVE-2026-105757Vllm · Vllm
Vulnerability data via NVD (ingested)
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.
External references
Search for exposed instances
Shodan + Censys queries derived from NVD's CPE data. The vuln tag catches assets Shodan has explicitly linked to this CVE; the product / banner fingerprints find exposed instances even when the vuln tag was never applied (which is common).
vuln:CVE-2026-105757product:"Vllm Vllm"http.html:"Vllm"More intel sources (5)
vuln:CVE-2026-105757vulnerabilities.cve_id: CVE-2026-105757CVE-2026-105757CVE-2026-105757"CVE-2026-105757" exploit -site:nvd.nist.gov