Vulnerability Description
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.
Related Weaknesses (CWE)
References
- https://github.com/vllm-project/vllm/commit/793cf79c89d4049124e756915468ac30318f
- 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
FAQ
What is CVE-2026-73557?
CVE-2026-73557 is a documented vulnerability. 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_invariant...
How severe is CVE-2026-73557?
CVSS scoring is not yet available for CVE-2026-73557. Check NVD for updates.
Is there a patch for CVE-2026-73557?
Check the references section above for vendor advisories and patch information. Review vendor security bulletins for remediation guidance.