Description (CNA)
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.
Affected (CNA)
| Vendor / product | Versions |
|---|---|
| vLLM / vLLM | affected 0.10.2 before 0.13.0; unaffected 0.13.0 |
CWE
CWE-20
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
ADP enrichment
No English description supplied.
No references supplied.
A flaw was found in vLLM. This vulnerability allows a remote attacker to trigger crashes or resource exhaustion, leading to a denial of service (DoS). By submitting specially crafted embedding requests with malformed tensor indices, when the `prompt-embeds` feature is enabled, an attacker could also potentially achieve out-of-bounds memory corruption, which may enable arbitrary code execution.