{"resultsPerPage":1,"startIndex":0,"totalResults":1,"format":"NVD_CVE","version":"2.0","timestamp":"2026-05-11T05:05:02.541","vulnerabilities":[{"cve":{"id":"CVE-2025-62164","sourceIdentifier":"security-advisories@github.com","published":"2025-11-21T02:15:43.193","lastModified":"2025-12-04T17:14:20.630","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1."}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","baseScore":8.8,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":2.8,"impactScore":5.9}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-20"},{"lang":"en","value":"CWE-123"},{"lang":"en","value":"CWE-502"},{"lang":"en","value":"CWE-787"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.10.2","versionEndExcluding":"0.11.1","matchCriteriaId":"257F44B9-5BDF-4A61-B7B9-A901DD438F9C"},{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:0.11.1:rc0:*:*:*:*:*:*","matchCriteriaId":"FEE054E1-1F84-4ACC-894C-D7E3652EF1B1"},{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:0.11.1:rc1:*:*:*:*:*:*","matchCriteriaId":"B05850DF-38FE-439F-9F7A-AA96DA9038CC"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/27204","source":"security-advisories@github.com","tags":["Issue Tracking","Patch","Vendor Advisory"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf","source":"security-advisories@github.com","tags":["Issue Tracking","Vendor Advisory"]}]}}]}