vLLM是vLLM团队开源的一个适用于 LLM 的高吞吐量和内存高效推理和服务引擎。 vLLM 0.20.2rc0版本至0.26.0之前版本存在竞争条件问题漏洞,该漏洞源于并发提交的prompt_embeds部分与torch.sparse.check_sparse_tensor_invariants的进程全局状态保存、启用和恢复操作产生竞争,可能导致无效稀疏张量到达tensor.to_dense,绕过CVE-2025-62164防护。
| Vendor | Product | Version Range | Status |
|---|---|---|---|
| vllm-project | vllm | >= 0.20.2rc0, < 0.26.0 |
affected |
Although we use advanced large model technology, its output may still contain inaccurate or outdated information.Shenlong tries to ensure data accuracy, but please verify and judge based on the actual situation.
| Vendor | Product | Affected Versions | CPE | Subscribe |
|---|---|---|---|---|
| vllm-project | vllm | >= 0.20.2rc0, < 0.26.0 | - |
|
| # | POC Description | Source Link | Shenlong Link |
|---|
No public POC found.
Login to generate AI POC| CVE-2026-73559 | 6.5 MEDIUM | vLLM: Completion prompt lists fan out into unbounded engine requests |
| CVE-2026-73555 | 5.3 MEDIUM | vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages |
| CVE-2026-73556 | 5.3 MEDIUM | vLLM: ReDoS via structured_outputs.regex in the lm-format-enforcer backend (no compile tim |
| CVE-2026-73558 | 5.3 MEDIUM | vLLM: Cross-User Data Leak Vulnerability |
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