Race Condition Affecting vllm-cu130 package, versions <0.26.0-r0
Threat Intelligence
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
Do your applications use this vulnerable package?
In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.
Test your applications- Snyk IDSNYK-MINIMOSLATEST-VLLMCU130-19634082
- published6 Sept 2026
- disclosed13 Aug 2026
Introduced: 13 Aug 2026
CVE-2026-73557 (opens in a new tab)How to fix?
Upgrade Minimos:latest vllm-cu130 to version 0.26.0-r0 or higher.
NVD Description
Note: Versions mentioned in the description apply only to the upstream vllm-cu130 package and not the vllm-cu130 package as distributed by Minimos.
See How to fix? for Minimos:latest relevant fixed versions and status.
vLLM is an inference and serving engine for large language models. From 0.20.2rc0 until 0.26.0, safe_load_prompt_embeds in vllm/renderers/embed_utils.py uses torch.sparse.check_sparse_tensor_invariants, whose process-global save, enable, and restore state can be raced by concurrent prompt_embeds parts submitted to POST /v1/chat/completions through AsyncMultiModalItemTracker.resolve_items, asyncio.gather, and the default executor, allowing an invalid sparse tensor to reach tensor.to_dense despite the CVE-2025-62164 guard when enable_prompt_embeds is enabled. This issue is fixed in version 0.26.0.
References
- https://www.cve.org/CVERecord?id=CVE-2026-73557
- https://github.com/advisories/GHSA-pr7f-p5mw-fc87
- https://osv.dev/vulnerability/MINI-j7xv-8w99-3p27
- https://osv.dev/vulnerability/CVE-2026-73557
- https://github.com/vllm-project/vllm/commit/793cf79c89d4049124e756915468ac30318f2e50
- https://github.com/vllm-project/vllm/pull/48583
- https://github.com/vllm-project/vllm/releases/tag/v0.26.0
- https://github.com/vllm-project/vllm/security/advisories/GHSA-pr7f-p5mw-fc87