- CVSS
- MEDIUM · 6.5v3.1CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Published
- 2026-10-05
- Weakness
- CWE-617
- Source
- nvd.nist.gov/vuln/detail/CVE-2026-105753
Description
vLLM is an inference and serving engine for large language models. Prior to 0.28.0, the default mirrored multimodal LRU cache can commit a media hash in the frontend sender cache during multimodal rendering and before engine admission, while the engine receiver cache never receives the payload if that request is rejected. A later request reusing the same media hash causes MultiModalProcessorSenderCache to send no payload and MultiModalReceiverCache to reach an assertion with the message “Expected a cached item,” producing a shared-service availability failure. This issue is fixed in version 0.28.0.
References
- https://github.com/vllm-project/vllm/commit/396204230423b7cc6798300926b8fa30190d26a9
- https://github.com/vllm-project/vllm/pull/46747
- https://github.com/vllm-project/vllm/pull/51897
- https://github.com/vllm-project/vllm/releases/tag/v0.28.0
- https://github.com/vllm-project/vllm/security/advisories/GHSA-ph3r-5jfg-f84f
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2026-105753 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.