{"resultsPerPage":71,"startIndex":0,"totalResults":71,"format":"NVD_CVE","version":"2.0","timestamp":"2026-08-31T19:38:07.905","vulnerabilities":[{"cve":{"id":"CVE-2024-4839","sourceIdentifier":"security@huntr.dev","published":"2024-06-24T13:15:11.900","lastModified":"2026-06-17T08:03:01.403","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"A Cross-Site Request Forgery (CSRF) vulnerability exists in the 'Servers Configurations' function of the parisneo/lollms-webui, versions 9.6 to the latest. The affected functions include Elastic search Service (under construction), XTTS service, Petals service, vLLM service, and Motion Ctrl service, which lack CSRF protection. This vulnerability allows attackers to deceive users into unwittingly installing the XTTS service among other packages by submitting a malicious installation request. Successful exploitation results in attackers tricking users into performing actions without their consent."},{"lang":"es","value":"Existe una vulnerabilidad de Cross-Site Request Forgery (CSRF) en la función 'Configuraciones de servidores' de parisneo/lollms-webui, versiones 9.6 a la última. Las funciones afectadas incluyen el servicio de búsqueda elástica (en construcción), el servicio XTTS, el servicio Petals, el servicio vLLM y el servicio Motion Ctrl, que carecen de protección CSRF. Esta vulnerabilidad permite a los atacantes engañar a los usuarios para que instalen involuntariamente el servicio XTTS entre otros paquetes enviando una solicitud de instalación maliciosa. La explotación exitosa da como resultado que los atacantes engañen a los usuarios para que realicen acciones sin su consentimiento."}],"affected":[{"source":"security@huntr.dev","affectedData":[{"vendor":"parisneo","product":"parisneo/lollms-webui","versions":[{"version":"unspecified","lessThanOrEqual":"latest","versionType":"custom","status":"affected"}]}]},{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","affectedData":[{"vendor":"parisneo","product":"lollms-webui","defaultStatus":"unknown","cpes":["cpe:2.3:a:parisneo:lollms-webui:9.6:*:*:*:*:*:*:*"],"versions":[{"version":"9.6","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:N","baseScore":3.3,"baseSeverity":"LOW","attackVector":"LOCAL","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"LOW","availabilityImpact":"NONE"},"exploitabilityScore":1.8,"impactScore":1.4}],"cvssMetricV30":[{"source":"security@huntr.dev","type":"Secondary","cvssData":{"version":"3.0","vectorString":"CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:L","baseScore":4.4,"baseSeverity":"MEDIUM","attackVector":"LOCAL","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"LOW","availabilityImpact":"LOW"},"exploitabilityScore":1.8,"impactScore":2.5}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2024-06-24T14:12:08.934209Z","id":"CVE-2024-4839","options":[{"exploitation":"poc"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security@huntr.dev","type":"Secondary","description":[{"lang":"en","value":"CWE-352"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:lollms:lollms-webui:9.6:*:*:*:*:*:*:*","matchCriteriaId":"8161B4F7-AE3F-4A7D-9B47-131C1AA3EC6F"}]}]}],"references":[{"url":"https://huntr.com/bounties/dcfc5a07-0427-42b5-a623-8d943873d7ff","source":"security@huntr.dev","tags":["Exploit","Third Party Advisory"]},{"url":"https://huntr.com/bounties/dcfc5a07-0427-42b5-a623-8d943873d7ff","source":"af854a3a-2127-422b-91ae-364da2661108","tags":["Exploit","Third Party Advisory"]}]}},{"cve":{"id":"CVE-2024-8768","sourceIdentifier":"secalert@redhat.com","published":"2024-09-17T17:15:11.100","lastModified":"2026-06-17T08:23:16.037","vulnStatus":"Deferred","cveTags":[],"descriptions":[{"lang":"en","value":"A flaw was found in the vLLM library. A completions API request with an empty prompt will crash the vLLM API server, resulting in a denial of service."},{"lang":"es","value":"Se encontró una falla en la librería vLLM. Una solicitud de API de finalización con un mensaje vacío bloqueará el servidor de API de vLLM, lo que provocará una denegación de servicio."}],"affected":[{"source":"secalert@redhat.com","affectedData":[{"defaultStatus":"unaffected","collectionURL":"https://github.com/vllm-project/vllm","packageName":"vllm","versions":[{"version":"0","lessThan":"0.5.5","versionType":"custom","status":"affected"}]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI)","defaultStatus":"affected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","packageName":"rhelai1/bootc-nvidia-rhel9","cpes":["cpe:/a:redhat:enterprise_linux_ai:1"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI)","defaultStatus":"affected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","packageName":"rhelai1/instructlab-nvidia-rhel9","cpes":["cpe:/a:redhat:enterprise_linux_ai:1"]}]}],"metrics":{"cvssMetricV31":[{"source":"secalert@redhat.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","baseScore":7.5,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":3.6}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2024-09-17T18:21:27.413720Z","id":"CVE-2024-8768","options":[{"exploitation":"poc"},{"automatable":"yes"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"secalert@redhat.com","type":"Secondary","description":[{"lang":"en","value":"CWE-617"}]}],"references":[{"url":"https://access.redhat.com/security/cve/CVE-2024-8768","source":"secalert@redhat.com"},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2311895","source":"secalert@redhat.com"},{"url":"https://github.com/vllm-project/vllm/issues/7632","source":"secalert@redhat.com"},{"url":"https://github.com/vllm-project/vllm/pull/7746","source":"secalert@redhat.com"}]}},{"cve":{"id":"CVE-2024-8939","sourceIdentifier":"secalert@redhat.com","published":"2024-09-17T17:15:11.327","lastModified":"2026-06-17T08:23:35.693","vulnStatus":"Deferred","cveTags":[],"descriptions":[{"lang":"en","value":"A vulnerability was found in the ilab model serve component, where improper handling of the best_of parameter in the vllm JSON web API can lead to a Denial of Service (DoS). The API used for LLM-based sentence or chat completion accepts a best_of parameter to return the best completion from several options. When this parameter is set to a large value, the API does not handle timeouts or resource exhaustion properly, allowing an attacker to cause a DoS by consuming excessive system resources. This leads to the API becoming unresponsive, preventing legitimate users from accessing the service."},{"lang":"es","value":"Se encontró una vulnerabilidad en el componente de servicio de modelos ilab, donde el manejo inadecuado del parámetro best_of en la API web JSON vllm puede provocar una denegación de servicio (DoS). La API utilizada para completar oraciones o chats basados en LLM acepta un parámetro best_of para devolver la mejor opción de varias opciones. Cuando este parámetro se establece en un valor alto, la API no maneja los tiempos de espera o el agotamiento de recursos de manera adecuada, lo que permite que un atacante provoque una denegación de servicio al consumir recursos excesivos del sistema. Esto hace que la API deje de responder, lo que impide que los usuarios legítimos accedan al servicio."}],"affected":[{"source":"secalert@redhat.com","affectedData":[{"defaultStatus":"unaffected","collectionURL":"https://github.com/vllm-project/vllm","packageName":"vllm","versions":[{"version":"0","lessThan":"0.5.0.post1","versionType":"custom","status":"affected"}]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI)","defaultStatus":"affected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","packageName":"rhelai1/bootc-nvidia-rhel9","cpes":["cpe:/a:redhat:enterprise_linux_ai:1"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI)","defaultStatus":"affected","collectionURL":"https://access.redhat.com/downloads/content/package-browser/","packageName":"rhelai1/instructlab-nvidia-rhel9","cpes":["cpe:/a:redhat:enterprise_linux_ai:1"]}]}],"metrics":{"cvssMetricV31":[{"source":"secalert@redhat.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","baseScore":6.2,"baseSeverity":"MEDIUM","attackVector":"LOCAL","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":2.5,"impactScore":3.6}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2024-09-17T19:51:11.286179Z","id":"CVE-2024-8939","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"secalert@redhat.com","type":"Secondary","description":[{"lang":"en","value":"CWE-400"}]}],"references":[{"url":"https://access.redhat.com/security/cve/CVE-2024-8939","source":"secalert@redhat.com"},{"url":"https://bugzilla.redhat.com/show_bug.cgi?id=2312782","source":"secalert@redhat.com"}]}},{"cve":{"id":"CVE-2025-24357","sourceIdentifier":"security-advisories@github.com","published":"2025-01-27T18:15:41.523","lastModified":"2026-06-17T08:58:39.307","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0."},{"lang":"es","value":"vLLM es una librería para la inferencia y el servicio de LLM. vllm/model_executor/weight_utils.py implementa hf_model_weights_iterator para cargar el punto de control del modelo, que se descarga desde huggingface. Utiliza la función Torch.load y el parámetro weights_only tiene el valor predeterminado Falso. Cuando Torch.load carga datos pickle maliciosos, ejecutará código arbitrario durante el desensamblaje. Esta vulnerabilidad se corrigió en la versión v0.7.0."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.7.0","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H","baseScore":7.5,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"HIGH","privilegesRequired":"NONE","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":1.6,"impactScore":5.9},{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H","baseScore":8.8,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":2.8,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-01-27T18:20:10.107207Z","id":"CVE-2025-24357","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionEndExcluding":"0.7.0","matchCriteriaId":"78210BFE-5D31-4D84-BA73-75C1594A3A3C"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/commit/d3d6bb13fb62da3234addf6574922a4ec0513d04","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/12366","source":"security-advisories@github.com","tags":["Issue Tracking","Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54","source":"security-advisories@github.com","tags":["Vendor Advisory"]},{"url":"https://pytorch.org/docs/stable/generated/torch.load.html","source":"security-advisories@github.com","tags":["Technical Description"]}]}},{"cve":{"id":"CVE-2025-25183","sourceIdentifier":"security-advisories@github.com","published":"2025-02-07T20:15:34.083","lastModified":"2026-06-17T09:00:26.433","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y uso eficiente de la memoria para LLM. Las declaraciones construidas de forma malintencionada pueden provocar colisiones de hash, lo que da como resultado la reutilización de la memoria caché, lo que puede interferir con las respuestas posteriores y provocar un comportamiento no deseado. El almacenamiento en caché de prefijos utiliza la función hash() incorporada de Python. A partir de Python 3.12, el comportamiento de hash(None) ha cambiado para ser un valor constante predecible. Esto hace que sea más factible que alguien pueda intentar explotar las colisiones de hash. El impacto de una colisión sería el uso de la memoria caché generada con un contenido diferente. Dado el conocimiento de los mensajes en uso y el comportamiento predecible del hash, alguien podría rellenar intencionalmente la memoria caché utilizando un mensaje que se sabe que colisiona con otro mensaje en uso. Este problema se ha solucionado en la versión 0.7.2 y se recomienda a todos los usuarios que actualicen. No existen workarounds para esta vulnerabilidad."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.7.2","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N","baseScore":2.6,"baseSeverity":"LOW","attackVector":"NETWORK","attackComplexity":"HIGH","privilegesRequired":"LOW","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"LOW","availabilityImpact":"NONE"},"exploitabilityScore":1.2,"impactScore":1.4}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-02-07T20:33:57.205558Z","id":"CVE-2025-25183","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-354"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionEndExcluding":"0.7.2","matchCriteriaId":"A5911C1A-F107-4B9B-BAE9-36A2B5181321"}]}]}],"references":[{"url":"https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7","source":"security-advisories@github.com","tags":["Not Applicable"]},{"url":"https://github.com/vllm-project/vllm/pull/12621","source":"security-advisories@github.com","tags":["Issue Tracking"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8","source":"security-advisories@github.com","tags":["Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-1953","sourceIdentifier":"cna@vuldb.com","published":"2025-03-04T20:15:37.657","lastModified":"2026-06-17T08:40:25.037","vulnStatus":"Deferred","cveTags":[],"descriptions":[{"lang":"en","value":"A vulnerability has been found in vLLM AIBrix 0.2.0 and classified as problematic. Affected by this vulnerability is an unknown functionality of the file pkg/plugins/gateway/prefixcacheindexer/hash.go of the component Prefix Caching. The manipulation leads to insufficiently random values. The complexity of an attack is rather high. The exploitation appears to be difficult. Upgrading to version 0.3.0 is able to address this issue. It is recommended to upgrade the affected component."},{"lang":"es","value":"Se ha encontrado una vulnerabilidad en vLLM AIBrix 0.2.0 y se ha clasificado como problemática. Esta vulnerabilidad afecta a una funcionalidad desconocida del archivo pkg/plugins/gateway/prefixcacheindexer/hash.go del componente Prefix Caching. La manipulación conduce a valores insuficientemente aleatorios. La complejidad de un ataque es bastante alta. La explotación parece ser difícil. La actualización a la versión 0.3.0 puede solucionar este problema. Se recomienda actualizar el componente afectado."}],"affected":[{"source":"cna@vuldb.com","affectedData":[{"vendor":"vLLM","product":"AIBrix","modules":["Prefix Caching"],"versions":[{"version":"0.2.0","status":"affected"}]}]}],"metrics":{"cvssMetricV40":[{"source":"cna@vuldb.com","type":"Secondary","cvssData":{"version":"4.0","vectorString":"CVSS:4.0/AV:A/AC:H/AT:N/PR:L/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X","baseScore":2.1,"baseSeverity":"LOW","attackVector":"ADJACENT","attackComplexity":"HIGH","attackRequirements":"NONE","privilegesRequired":"LOW","userInteraction":"NONE","vulnConfidentialityImpact":"LOW","vulnIntegrityImpact":"NONE","vulnAvailabilityImpact":"NONE","subConfidentialityImpact":"NONE","subIntegrityImpact":"NONE","subAvailabilityImpact":"NONE","exploitMaturity":"NOT_DEFINED","confidentialityRequirement":"NOT_DEFINED","integrityRequirement":"NOT_DEFINED","availabilityRequirement":"NOT_DEFINED","modifiedAttackVector":"NOT_DEFINED","modifiedAttackComplexity":"NOT_DEFINED","modifiedAttackRequirements":"NOT_DEFINED","modifiedPrivilegesRequired":"NOT_DEFINED","modifiedUserInteraction":"NOT_DEFINED","modifiedVulnConfidentialityImpact":"NOT_DEFINED","modifiedVulnIntegrityImpact":"NOT_DEFINED","modifiedVulnAvailabilityImpact":"NOT_DEFINED","modifiedSubConfidentialityImpact":"NOT_DEFINED","modifiedSubIntegrityImpact":"NOT_DEFINED","modifiedSubAvailabilityImpact":"NOT_DEFINED","Safety":"NOT_DEFINED","Automatable":"NOT_DEFINED","Recovery":"NOT_DEFINED","valueDensity":"NOT_DEFINED","vulnerabilityResponseEffort":"NOT_DEFINED","providerUrgency":"NOT_DEFINED"}}],"cvssMetricV31":[{"source":"cna@vuldb.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:A/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:N","baseScore":2.6,"baseSeverity":"LOW","attackVector":"ADJACENT_NETWORK","attackComplexity":"HIGH","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"LOW","integrityImpact":"NONE","availabilityImpact":"NONE"},"exploitabilityScore":1.2,"impactScore":1.4}],"cvssMetricV2":[{"source":"cna@vuldb.com","type":"Secondary","cvssData":{"version":"2.0","vectorString":"AV:A/AC:H/Au:S/C:P/I:N/A:N","baseScore":1.4,"accessVector":"ADJACENT_NETWORK","accessComplexity":"HIGH","authentication":"SINGLE","confidentialityImpact":"PARTIAL","integrityImpact":"NONE","availabilityImpact":"NONE"},"baseSeverity":"LOW","exploitabilityScore":2.5,"impactScore":2.9,"acInsufInfo":false,"obtainAllPrivilege":false,"obtainUserPrivilege":false,"obtainOtherPrivilege":false,"userInteractionRequired":false}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-03-04T20:36:04.715343Z","id":"CVE-2025-1953","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"cna@vuldb.com","type":"Secondary","description":[{"lang":"en","value":"CWE-310"},{"lang":"en","value":"CWE-330"}]}],"references":[{"url":"https://github.com/vllm-project/aibrix/issues/749","source":"cna@vuldb.com"},{"url":"https://github.com/vllm-project/aibrix/issues/749#event-16488517974","source":"cna@vuldb.com"},{"url":"https://github.com/vllm-project/aibrix/pull/752","source":"cna@vuldb.com"},{"url":"https://github.com/vllm-project/aibrix/pull/752/commits/3d25d95aebd66f24a549200edcebc5ea423b317a","source":"cna@vuldb.com"},{"url":"https://vuldb.com/?ctiid.298543","source":"cna@vuldb.com"},{"url":"https://vuldb.com/?id.298543","source":"cna@vuldb.com"},{"url":"https://vuldb.com/?submit.509958","source":"cna@vuldb.com"}]}},{"cve":{"id":"CVE-2025-29770","sourceIdentifier":"security-advisories@github.com","published":"2025-03-19T16:15:31.977","lastModified":"2026-06-17T09:05:38.477","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. The outlines library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server. The affected code in vLLM is vllm/model_executor/guided_decoding/outlines_logits_processors.py, which unconditionally uses the cache from outlines. A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space. Note that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the guided_decoding_backend key of the extra_body field of the request. This issue applies only to the V0 engine and is fixed in 0.8.0."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en memoria para LLM. La librería de esquemas es uno de los backends que vLLM utiliza para la salida estructurada (también conocida como decodificación guiada). Outlines proporciona una caché opcional para sus gramáticas compiladas en el sistema de archivos local. Esta caché está activada por defecto en vLLM. Outlines también está disponible por defecto a través del servidor de API compatible con OpenAI. El código afectado en vLLM es vllm/model_executor/guided_decoding/outlines_logits_processors.py, que utiliza incondicionalmente la caché de outlines. Un usuario malintencionado puede enviar un flujo de solicitudes de decodificación muy cortas con esquemas únicos, lo que resulta en una adición a la caché para cada solicitud. Esto puede provocar una denegación de servicio si el sistema de archivos se queda sin espacio. Tenga en cuenta que, incluso si vLLM se configuró para usar un backend diferente por defecto, aún es posible seleccionar esquemas por solicitud mediante la clave `guided_decoding_backend` del campo `extra_body` de la solicitud. Este problema solo afecta al motor V0 y se solucionó en la versión 0.8.0."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.8.0","status":"affected"}]}]}],"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:N/I:N/A:H","baseScore":6.5,"baseSeverity":"MEDIUM","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":2.8,"impactScore":3.6}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-03-19T20:14:04.764365Z","id":"CVE-2025-29770","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-770"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionEndExcluding":"0.8.0","matchCriteriaId":"4596758A-5F3D-4330-BB37-EEF73CC90D9E"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py","source":"security-advisories@github.com","tags":["Product"]},{"url":"https://github.com/vllm-project/vllm/pull/14837","source":"security-advisories@github.com","tags":["Issue Tracking","Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-mgrm-fgjv-mhv8","source":"security-advisories@github.com","tags":["Patch","Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-29783","sourceIdentifier":"security-advisories@github.com","published":"2025-03-19T16:15:32.477","lastModified":"2026-06-17T09:05:39.830","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. When vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP on all network interfaces will allow attackers to execute remote code on distributed hosts. This is a remote code execution vulnerability impacting any deployments using Mooncake to distribute KV across distributed hosts. This vulnerability is fixed in 0.8.0."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en el uso de memoria para LLM. Cuando vLLM se configura para usar Mooncake, la deserialización insegura expuesta directamente a través de ZMQ/TCP en todas las interfaces de red permitirá a los atacantes ejecutar código remoto en hosts distribuidos. Esta vulnerabilidad de ejecución remota de código afecta a cualquier implementación que use Mooncake para distribuir KV entre hosts distribuidos. 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The function uses pickle.loads to parse received sockets directly, leading to a remote code execution vulnerability. An attacker can exploit this by sending a malicious payload to the MessageQueue, causing the victim's machine to execute arbitrary code."},{"lang":"es","value":"vllm-project vllm versión v0.6.2 contiene una vulnerabilidad en la función de la API MessageQueue.dequeue(). Esta función utiliza pickle.loads para analizar directamente los sockets recibidos, lo que genera una vulnerabilidad de ejecución remota de código. Un atacante puede explotar esto enviando un payload a MessageQueue, lo que provoca que el equipo de la víctima ejecute código arbitrario."}],"affected":[{"source":"security@huntr.dev","affectedData":[{"vendor":"vllm-project","product":"vllm-project/vllm","versions":[{"version":"unspecified","lessThanOrEqual":"latest","versionType":"custom","status":"affected"}]}]}],"metrics":{"cvssMetricV30":[{"source":"security@huntr.dev","type":"Secondary","cvssData":{"version":"3.0","vectorString":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","baseScore":9.8,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-03-20T17:51:10.653784Z","id":"CVE-2024-11041","options":[{"exploitation":"poc"},{"automatable":"yes"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security@huntr.dev","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:0.6.2:*:*:*:*:*:*:*","matchCriteriaId":"5C723AC6-7D43-4776-B486-9F870A5645A6"}]}]}],"references":[{"url":"https://huntr.com/bounties/00136195-11e0-4ad0-98d5-72db066e867f","source":"security@huntr.dev","tags":["Exploit","Third Party Advisory"]}]}},{"cve":{"id":"CVE-2024-9053","sourceIdentifier":"security@huntr.dev","published":"2025-03-20T10:15:46.327","lastModified":"2026-06-17T08:23:51.753","vulnStatus":"Modified","cveTags":[],"descriptions":[{"lang":"en","value":"vllm-project vllm version 0.6.0 contains a vulnerability in the AsyncEngineRPCServer() RPC server entrypoints. The core functionality run_server_loop() calls the function _make_handler_coro(), which directly uses cloudpickle.loads() on received messages without any sanitization. This can result in remote code execution by deserializing malicious pickle data."},{"lang":"es","value":"vllm-project vllm versión 0.6.0 contiene una vulnerabilidad en los puntos de entrada del servidor RPC AsyncEngineRPCServer(). La función principal, run_server_loop(), llama a la función _make_handler_coro(), que utiliza directamente cloudpickle.loads() en los mensajes recibidos sin ningún tipo de depuración. Esto puede provocar la ejecución remota de código al deserializar datos de pickle maliciosos."}],"affected":[{"source":"security@huntr.dev","affectedData":[{"vendor":"vllm-project","product":"vllm-project/vllm","versions":[{"version":"unspecified","lessThanOrEqual":"latest","versionType":"custom","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","baseScore":9.8,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":5.9}],"cvssMetricV30":[{"source":"security@huntr.dev","type":"Secondary","cvssData":{"version":"3.0","vectorString":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","baseScore":9.8,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-03-20T17:51:26.302436Z","id":"CVE-2024-9053","options":[{"exploitation":"poc"},{"automatable":"yes"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security@huntr.dev","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]},{"source":"nvd@nist.gov","type":"Secondary","description":[{"lang":"en","value":"CWE-78"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm-project:vllm:0.6.0:*:*:*:*:*:*:*","matchCriteriaId":"C21B072B-4EBD-4D1A-B27A-62ED9D7D9170"}]}]}],"references":[{"url":"https://huntr.com/bounties/75a544f3-34a3-4da0-b5a3-1495cb031e09","source":"security@huntr.dev","tags":["Exploit","Third Party Advisory"]}]}},{"cve":{"id":"CVE-2025-30202","sourceIdentifier":"security-advisories@github.com","published":"2025-04-30T01:15:51.800","lastModified":"2026-06-17T09:08:20.873","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en memoria para LLM. Las versiones a partir de la 0.5.2 y anteriores a la 0.8.5 son vulnerables a denegación de servicio y exposición de datos a través de ZeroMQ en implementaciones de vLLM multinodo. En una implementación de vLLM multinodo, vLLM utiliza ZeroMQ para algunos fines de comunicación multinodo. El host vLLM principal abre un socket XPUB ZeroMQ y lo vincula a TODAS las interfaces. Si bien el socket siempre está abierto para implementaciones multinodo, solo se utiliza al realizar paralelismo tensorial en varios hosts. Cualquier cliente con acceso de red a este host puede conectarse a este socket XPUB a menos que su puerto esté bloqueado por un firewall. Una vez conectados, estos clientes arbitrarios recibirán los mismos datos transmitidos a todos los hosts vLLM secundarios. Estos datos son información interna del estado de vLLM que no es útil para un atacante. Al conectarse a este socket muchas veces y no leer los datos publicados, un atacante también puede causar una denegación de servicio al ralentizar o incluso bloquear al publicador. Este problema se ha corregido en la versión 0.8.5."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.5.2, < 0.8.5","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","baseScore":7.5,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":3.6},{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","baseScore":7.5,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":3.6}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-04-30T13:16:29.868734Z","id":"CVE-2025-30202","options":[{"exploitation":"none"},{"automatable":"yes"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-770"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.5.2","versionEndExcluding":"0.8.5","matchCriteriaId":"15AC3826-5B31-40E2-9964-6F8930043285"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/commit/a0304dc504c85f421d38ef47c64f83046a13641c","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/6183","source":"security-advisories@github.com","tags":["Issue Tracking","Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-9f8f-2vmf-885j","source":"security-advisories@github.com","tags":["Exploit","Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-32444","sourceIdentifier":"security-advisories@github.com","published":"2025-04-30T01:15:51.953","lastModified":"2026-06-17T09:12:00.180","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en memoria para LLM. Las versiones a partir de la 0.6.5 y anteriores a la 0.8.5, que integran vLLM con mooncake, son vulnerables a la ejecución remota de código debido al uso de serialización basada en pickle sobre sockets ZeroMQ no seguros. Los sockets vulnerables estaban configurados para escuchar en todas las interfaces de red, lo que aumenta la probabilidad de que un atacante pueda acceder a los sockets ZeroMQ vulnerables para ejecutar un ataque. Las instancias de vLLM que no utilizan la integración con mooncake no son vulnerables. Este problema se ha corregido en la versión 0.8.5."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.6.5, < 0.8.5","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","baseScore":10.0,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"CHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":6.0},{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","baseScore":9.8,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-04-30T13:08:21.425422Z","id":"CVE-2025-32444","options":[{"exploitation":"none"},{"automatable":"yes"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.6.5","versionEndExcluding":"0.8.5","matchCriteriaId":"24BAE45E-0FCF-4E74-953A-88F12E093C0F"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179","source":"security-advisories@github.com","tags":["Product"]},{"url":"https://github.com/vllm-project/vllm/commit/a5450f11c95847cf51a17207af9a3ca5ab569b2c","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5","source":"security-advisories@github.com","tags":["Exploit","Vendor Advisory"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7","source":"security-advisories@github.com","tags":["Not Applicable"]}]}},{"cve":{"id":"CVE-2025-46560","sourceIdentifier":"security-advisories@github.com","published":"2025-04-30T01:15:52.097","lastModified":"2026-06-17T09:26:37.837","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the algorithm exhibits ​​quadratic time complexity (O(n²))​​, allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio de alto rendimiento y eficiente en memoria para LLM. Las versiones a partir de la 0.8.0 y anteriores a la 0.8.5 se ven afectadas por una vulnerabilidad crítica de rendimiento en la lógica de preprocesamiento de entrada del tokenizador multimodal. El código reemplaza dinámicamente los tokens de marcador de posición (p. ej., &lt;|audio_|&gt;, &lt;|image_|&gt;) con tokens repetidos basados ??en longitudes precalculadas. Debido a las ineficientes operaciones de concatenación de listas, el algoritmo presenta una complejidad temporal cuadrática (O(n²)), lo que permite a los actores maliciosos activar el agotamiento de recursos mediante entradas especialmente manipuladas. Este problema se ha corregido en la versión 0.8.5."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.8.0, < 0.8.5","status":"affected"}]}]}],"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:N/I:N/A:H","baseScore":6.5,"baseSeverity":"MEDIUM","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":2.8,"impactScore":3.6},{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","baseScore":7.5,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":3.6}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-04-30T13:09:10.349287Z","id":"CVE-2025-46560","options":[{"exploitation":"poc"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-1333"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.8.0","versionEndExcluding":"0.8.5","matchCriteriaId":"19C6D0C7-632B-4AA7-97E5-CCF21EC350E5"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197","source":"security-advisories@github.com","tags":["Product"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg","source":"security-advisories@github.com","tags":["Exploit","Vendor Advisory"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg","source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","tags":["Exploit","Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-30165","sourceIdentifier":"security-advisories@github.com","published":"2025-05-06T17:16:11.660","lastModified":"2026-06-17T09:08:16.850","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio para modelos de lenguaje extensos. En una implementación de vLLM multinodo con el motor V0, vLLM utiliza ZeroMQ para la comunicación multinodo. Los hosts secundarios de vLLM abren un socket \"SUB\" de ZeroMQ y se conectan a un socket \"XPUB\" en el host principal de vLLM. Cuando se reciben datos en este socket \"SUB\", se deserializan con \"pickle\". Esto es peligroso, ya que puede utilizarse para ejecutar código en una máquina remota. Dado que la vulnerabilidad existe en un cliente que se conecta al host principal de vLLM, sirve como punto de escalada. Si el host principal de vLLM se ve comprometido, esta vulnerabilidad podría utilizarse para comprometer el resto de los hosts de la implementación de vLLM. Los atacantes también podrían utilizar otros medios para explotar la vulnerabilidad sin necesidad de acceder al host principal de vLLM. Un ejemplo sería el uso de envenenamiento de caché ARP para redirigir el tráfico a un endpoint malicioso utilizado para entregar un payload con código arbitrario que se ejecuta en la máquina objetivo. Tenga en cuenta que este problema solo afecta al motor V0, que ha estado desactivado por defecto desde la versión v0.8.0. Además, el problema solo se aplica a implementaciones que utilizan paralelismo tensorial en varios hosts, lo cual no esperamos que sea un patrón de implementación común. Dado que V0 ha estado desactivado por defecto desde la versión v0.8.0 y la solución es bastante invasiva, los responsables de vLLM han decidido no corregir este problema. En su lugar, recomiendan a los usuarios que se aseguren de que su entorno esté en una red segura en caso de que se utilice este patrón. El motor V1 no se ve afectado por este problema."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.5.2, <= 0.8.5.post1","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","baseScore":8.0,"baseSeverity":"HIGH","attackVector":"ADJACENT_NETWORK","attackComplexity":"LOW","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":2.1,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-05-06T17:22:47.717996Z","id":"CVE-2025-30165","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.5.2","matchCriteriaId":"E2646F2B-C4B5-4D2B-B8E0-4113504AD8FF"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L295-L301","source":"security-advisories@github.com","tags":["Product"]},{"url":"https://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L468-L470","source":"security-advisories@github.com","tags":["Product"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-9pcc-gvx5-r5wm","source":"security-advisories@github.com","tags":["Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-47277","sourceIdentifier":"security-advisories@github.com","published":"2025-05-20T18:15:46.730","lastModified":"2026-06-17T09:27:39.253","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side.​ The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured."},{"lang":"es","value":"vLLM, un motor de inferencia y servicio para modelos de lenguaje grandes (LLM), presenta un problema en las versiones 0.6.5 a 0.8.4 que SOLO afecta a entornos que utilizan la integración de transferencia de caché KV `PyNcclPipe` con el motor V0. Ninguna otra configuración se ve afectada. vLLM admite el uso de la clase `PyNcclPipe` para establecer un dominio de comunicación punto a punto para la transmisión de datos entre nodos distribuidos. La transmisión de caché KV del lado de la GPU se implementa mediante la clase `PyNcclCommunicator`, mientras que el paso de mensajes de control del lado de la CPU se gestiona mediante los métodos `send_obj` y `recv_obj` en el lado de la CPU. El objetivo era que esta interfaz solo se expusiera a una red privada utilizando la dirección IP especificada por el parámetro de CLI `--kv-ip`. La documentación de vLLM explica cómo esto debe limitarse a una red segura. El comportamiento predeterminado e intencional de PyTorch es que la interfaz `TCPStore` escucha en TODAS las interfaces, independientemente de la dirección IP proporcionada. La dirección IP proporcionada solo se usaba como dirección del cliente. vLLM se corrigió para usar una solución alternativa que obligaba a la instancia `TCPStore` a vincular su socket a una interfaz privada específica. A partir de la versión 0.8.5, vLLM limita el socket `TCPStore` a la interfaz privada configurada."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.6.5, < 0.8.5","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","baseScore":9.8,"baseSeverity":"CRITICAL","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"HIGH","integrityImpact":"HIGH","availabilityImpact":"HIGH"},"exploitabilityScore":3.9,"impactScore":5.9}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-05-20T17:52:22.643444Z","id":"CVE-2025-47277","options":[{"exploitation":"none"},{"automatable":"yes"},{"technicalImpact":"total"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-502"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.6.5","versionEndExcluding":"0.8.5","matchCriteriaId":"24BAE45E-0FCF-4E74-953A-88F12E093C0F"}]}]}],"references":[{"url":"https://docs.vllm.ai/en/latest/deployment/security.html","source":"security-advisories@github.com","tags":["Technical Description"]},{"url":"https://github.com/vllm-project/vllm/commit/0d6e187e88874c39cda7409cf673f9e6546893e7","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/15988","source":"security-advisories@github.com","tags":["Issue Tracking","Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-hjq4-87xh-g4fv","source":"security-advisories@github.com","tags":["Exploit","Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-46570","sourceIdentifier":"security-advisories@github.com","published":"2025-05-29T17:15:21.327","lastModified":"2026-06-17T09:26:38.523","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0."},{"lang":"es","value":"vLLM es un motor de inferencia y entrega para modelos de lenguaje grandes (LLM). Antes de la versión 0.9.0, al procesar una nueva solicitud, si el mecanismo PageAttention encuentra un fragmento de prefijo coincidente, el proceso de precompletado se acelera, lo que se refleja en el TTFT (Tiempo hasta el Primer Token). Estas diferencias de tiempo causadas por la coincidencia de fragmentos son lo suficientemente significativas como para ser detectadas y explotadas. Este problema se ha corregido en la versión 0.9.0."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":"< 0.9.0","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N","baseScore":2.6,"baseSeverity":"LOW","attackVector":"NETWORK","attackComplexity":"HIGH","privilegesRequired":"LOW","userInteraction":"REQUIRED","scope":"UNCHANGED","confidentialityImpact":"LOW","integrityImpact":"NONE","availabilityImpact":"NONE"},"exploitabilityScore":1.2,"impactScore":1.4}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-05-29T18:04:57.706360Z","id":"CVE-2025-46570","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-208"}]},{"source":"nvd@nist.gov","type":"Primary","description":[{"lang":"en","value":"CWE-203"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionEndExcluding":"0.9.0","matchCriteriaId":"A8F1E19D-D7C6-477D-B737-277EF3E3F20F"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/commit/77073c77bc2006eb80ea6d5128f076f5e6c6f54f","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/17045","source":"security-advisories@github.com","tags":["Issue Tracking","Vendor Advisory"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-4qjh-9fv9-r85r","source":"security-advisories@github.com","tags":["Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-46722","sourceIdentifier":"security-advisories@github.com","published":"2025-05-29T17:15:21.523","lastModified":"2026-06-17T09:26:52.583","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLM). En versiones desde la 0.7.0 hasta anteriores a la 0.9.0, en el archivo vllm/multimodal/hasher.py, la clase MultiModalHasher presenta un problema de seguridad e integridad de datos en su método de hash de imágenes. Actualmente, serializa los objetos PIL.Image.Image utilizando únicamente obj.tobytes(), que devuelve únicamente los datos de píxeles sin procesar, sin incluir metadatos como la forma de la imagen (ancho, alto, modo). Como resultado, dos imágenes de diferentes tamaños (p. ej., 30x100 y 100x30) con la misma secuencia de bytes de píxeles podrían generar el mismo valor hash. Esto puede provocar colisiones de hash, aciertos de caché incorrectos e incluso fugas de datos o riesgos de seguridad. Este problema se ha corregido en la versión 0.9.0."}],"affected":[{"source":"security-advisories@github.com","affectedData":[{"vendor":"vllm-project","product":"vllm","versions":[{"version":">= 0.7.0, < 0.9.0","status":"affected"}]}]}],"metrics":{"cvssMetricV31":[{"source":"security-advisories@github.com","type":"Secondary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L","baseScore":4.2,"baseSeverity":"MEDIUM","attackVector":"NETWORK","attackComplexity":"HIGH","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"LOW","integrityImpact":"NONE","availabilityImpact":"LOW"},"exploitabilityScore":1.6,"impactScore":2.5},{"source":"nvd@nist.gov","type":"Primary","cvssData":{"version":"3.1","vectorString":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L","baseScore":7.3,"baseSeverity":"HIGH","attackVector":"NETWORK","attackComplexity":"LOW","privilegesRequired":"NONE","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"LOW","integrityImpact":"LOW","availabilityImpact":"LOW"},"exploitabilityScore":3.9,"impactScore":3.4}],"ssvcV203":[{"source":"134c704f-9b21-4f2e-91b3-4a467353bcc0","ssvcData":{"timestamp":"2025-05-29T18:12:29.713264Z","id":"CVE-2025-46722","options":[{"exploitation":"none"},{"automatable":"no"},{"technicalImpact":"partial"}],"role":"CISA Coordinator","version":"2.0.3"}}]},"weaknesses":[{"source":"security-advisories@github.com","type":"Secondary","description":[{"lang":"en","value":"CWE-1023"},{"lang":"en","value":"CWE-1288"}]}],"configurations":[{"nodes":[{"operator":"OR","negate":false,"cpeMatch":[{"vulnerable":true,"criteria":"cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*","versionStartIncluding":"0.7.0","versionEndExcluding":"0.9.0","matchCriteriaId":"08DBEEAC-7BAC-4A44-894B-5F544B5CF9D3"}]}]}],"references":[{"url":"https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848","source":"security-advisories@github.com","tags":["Patch"]},{"url":"https://github.com/vllm-project/vllm/pull/17378","source":"security-advisories@github.com","tags":["Issue Tracking","Patch"]},{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6","source":"security-advisories@github.com","tags":["Vendor Advisory"]}]}},{"cve":{"id":"CVE-2025-48887","sourceIdentifier":"security-advisories@github.com","published":"2025-05-30T18:15:32.500","lastModified":"2026-06-17T09:30:26.173","vulnStatus":"Analyzed","cveTags":[],"descriptions":[{"lang":"en","value":"vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue."},{"lang":"es","value":"vLLM, un motor de inferencia y servicio para modelos de lenguaje grandes (LLM), presenta una vulnerabilidad de denegación de servicio por expresión regular (ReDoS) en el archivo `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` de las versiones 0.6.4 a 0.9.0, excepto esta última. La causa principal es el uso de una expresión regular anidada y altamente compleja para la detección de llamadas a herramientas, que un atacante puede explotar para causar una degradación grave del rendimiento o inhabilitar el servicio. El patrón contiene múltiples cuantificadores anidados, grupos opcionales y repeticiones internas, lo que lo hace vulnerable a un retroceso catastrófico. 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In version 0.8.0 up to but excluding 0.9.0, the vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the \"pattern\" and \"type\" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. Version 0.9.0 fixes the issue."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLM). Desde la versión 0.8.0 hasta la 0.9.0 (excluyendo esta última), el backend de vLLM utilizado con el endpoint de OpenAPI /v1/chat/completions no valida entradas inesperadas o incorrectas en los campos \"patrón\" y \"tipo\" al invocar la funcionalidad de herramientas. Estas entradas no se validan antes de compilarse o analizarse, lo que provoca un bloqueo del trabajador de inferencia con una sola solicitud. El trabajador permanece inactivo hasta que se reinicia. 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For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue."},{"lang":"es","value":"vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLM). Antes de la versión 0.14.1, existe una vulnerabilidad de falsificación de petición del lado del servidor (SSRF) en la clase 'MediaConnector' dentro del conjunto de características multimodales del proyecto vLLM. Los métodos load_from_url y load_from_url_async obtienen y procesan medios de URLs proporcionadas por los usuarios, utilizando diferentes librerías de análisis de Python al restringir el host de destino. Estas dos librerías de análisis tienen diferentes interpretaciones de las barras invertidas, lo que permite eludir la restricción del nombre de host. Esto permite a un atacante coaccionar al servidor vLLM para que realice peticiones arbitrarias a recursos de red internos. Esta vulnerabilidad es particularmente crítica en entornos contenerizados como 'llm-d', donde un pod vLLM comprometido podría usarse para escanear la red interna, interactuar con otros pods y potencialmente causar denegación de servicio o acceder a datos sensibles. Por ejemplo, un atacante podría hacer que el pod vLLM envíe peticiones maliciosas a un endpoint de gestión interno de 'llm-d', lo que llevaría a la inestabilidad del sistema al informar falsamente métricas como el estado de la caché KV. 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