AI Threat Alert
If your organization runs vLLM for multimodal inference, patch to 0.11.1 immediately — any authenticated API user can crash the entire serving engine with a single malformed request, taking down all dependent services. This is a hard availability risk with no workaround other than restricting API access to fully trusted callers. Patch-or-restrict is the only acceptable posture.
Affected Systems
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| vllm | pip | >= 0.5.5, < 0.11.1 | 0.11.1 |
| vllm | pip | — | No patch |
| vllm | pip | — | No patch |
| vllm | pip | — | No patch |
Severity & Risk
Recommended Action
- 1) Patch: upgrade vLLM to >= 0.11.1 (pip install vllm==0.11.1). 2) If patching is delayed, restrict vLLM API access to known trusted callers via network policy or API gateway — remove low-privilege or anonymous access. 3) Add input validation at the API gateway layer to reject embedding payloads with unexpected shape dimensions before they reach vLLM. 4) Implement process supervision (systemd, Kubernetes liveness probes) to auto-restart the vLLM engine on crash and alert on restart events. 5) Monitor vLLM process crash logs for unexpected terminations as a detection signal for exploitation attempts.
Classification
Compliance Impact
This CVE is relevant to:
Technical Details
NVD Description
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page). This issue has been patched in version 0.11.1.
Exploitation Scenario
An attacker with any level of API access to a vLLM multimodal endpoint — including a free-tier or internal dev account — crafts a POST request to the inference API submitting a multimodal embedding tensor with the correct number of dimensions (correct ndim) but wrong hidden dimension size. vLLM's improper array index validation (CWE-129) fails to catch the shape mismatch, causing an unhandled exception that crashes the engine process. The attacker can repeat this in a loop to cause sustained denial of service, or use it as a one-shot to disrupt a critical inference pipeline during a sensitive business window.
Weaknesses (CWE)
CVSS Vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H References
- github.com/advisories/GHSA-pmqf-x6x8-p7qw
- github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b
- github.com/vllm-project/vllm/pull/27204
- github.com/vllm-project/vllm/pull/6613
- github.com/vllm-project/vllm/security/advisories/GHSA-pmqf-x6x8-p7qw
- nvd.nist.gov/vuln/detail/CVE-2025-62372
- github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b Patch
- github.com/vllm-project/vllm/pull/27204 Issue Patch Vendor
- github.com/vllm-project/vllm/pull/6613 Issue
- github.com/vllm-project/vllm/security/advisories/GHSA-pmqf-x6x8-p7qw Mitigation Vendor