CVE•Published 2026-06-20•Modified 2026-07-15•1 article on news•5 live references•NVD data

CVE-2026-56340Vllm · Vllm

Vulnerability data via NVD (ingested)

CVSS v3.1
8.8
HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
EPSS percentile
27
Exploit Prediction Scoring System · top 73% of all CVEs
Description

vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing. Because PyTorch disables sparse tensor invariant checks by default, an attacker can submit crafted embedding requests with malformed (negative or out-of-bounds) tensor indices, when the prompt-embeds feature is enabled, to trigger crashes or resource exhaustion (denial of service), with potential for out-of-bounds/write-what-where memory corruption. This continues CVE-2025-62164, whose prior fix only disabled the feature by default rather than addressing the root cause.

Timeline
Published 2026-06-20
Modified 2026-07-15

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).

More intel sources (5)

Known PoCs on GitHub

No public proof-of-concept repositories found for CVE-2026-56340 on GitHub.