{"dataType":"CVE_RECORD","dataVersion":"5.2","cveMetadata":{"cveId":"CVE-2026-54234","assignerOrgId":"a0819718-46f1-4df5-94e2-005712e83aaa","state":"PUBLISHED","assignerShortName":"GitHub_M","dateReserved":"2026-06-12T16:25:43.084Z","datePublished":"2026-07-06T19:49:20.481Z","dateUpdated":"2026-07-07T14:13:34.348Z"},"containers":{"cna":{"title":"vLLM: Remote DoS in vLLM via Invalid Recovered Token Reinjection","problemTypes":[{"descriptions":[{"cweId":"CWE-20","lang":"en","description":"CWE-20: Improper Input Validation","type":"CWE"}]},{"descriptions":[{"cweId":"CWE-1284","lang":"en","description":"CWE-1284: Improper Validation of Specified Quantity in Input","type":"CWE"}]}],"metrics":[{"cvssV3_1":{"attackComplexity":"LOW","attackVector":"NETWORK","availabilityImpact":"HIGH","baseScore":7.5,"baseSeverity":"HIGH","confidentialityImpact":"NONE","integrityImpact":"NONE","privilegesRequired":"NONE","scope":"UNCHANGED","userInteraction":"NONE","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","version":"3.1"}}],"references":[{"name":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f","tags":["x_refsource_CONFIRM"],"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f"},{"name":"https://github.com/vllm-project/vllm/pull/44744","tags":["x_refsource_MISC"],"url":"https://github.com/vllm-project/vllm/pull/44744"},{"name":"https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca","tags":["x_refsource_MISC"],"url":"https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca"}],"affected":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.24.0","status":"affected"}]}],"providerMetadata":{"orgId":"a0819718-46f1-4df5-94e2-005712e83aaa","shortName":"GitHub_M","dateUpdated":"2026-07-06T19:49:20.481Z"},"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, a frontend-legal multi-request speculative decoding workload can cause the rejection sampler to produce a recovered token equal to the model vocabulary size boundary value, which is then converted to negative one when the engine selects the next live token for a request and is written back into the drafter's input ids; that out-of-vocabulary value is later consumed by the model's embedding and attention path and crashes the engine worker with a GPU device-side assertion. The same triggering request sequence is reachable through the public gRPC Generate and Abort endpoints, so a remote client that can send generation requests can crash the shared engine worker, aborting concurrent requests and causing a service-wide denial of service for other clients of the deployment until the worker is restarted. This issue is fixed in version 0.24.0."}],"source":{"advisory":"GHSA-8wr5-jm2h-8r4f","discovery":"UNKNOWN"}},"adp":[{"references":[{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f","tags":["exploit"]}],"metrics":[{"other":{"type":"ssvc","content":{"timestamp":"2026-07-07T14:12:43.565166Z","id":"CVE-2026-54234","options":[{"Exploitation":"poc"},{"Automatable":"yes"},{"Technical Impact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}}],"title":"CISA ADP Vulnrichment","providerMetadata":{"orgId":"134c704f-9b21-4f2e-91b3-4a467353bcc0","shortName":"CISA-ADP","dateUpdated":"2026-07-07T14:13:34.348Z"}}]}}