- CVSS
- HIGH · 8.8v3.1CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
- Published
- 2026-06-20
- Weakness
- CWE-20, CWE-787
- Source
- nvd.nist.gov/vuln/detail/CVE-2026-56340
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.
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-mcmc-2m55-j8jj
- https://www.vulncheck.com/advisories/vllm-denial-of-service-via-unvalidated-multimodal-embeddings
- https://access.redhat.com/security/cve/CVE-2026-56340
- https://bugzilla.redhat.com/show_bug.cgi?id=2491060
- https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-56340.json
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2026-56340 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.