{"dataType": "CVE_RECORD", "dataVersion": "5.2", "cveMetadata": {"cveId": "CVE-2026-34753", "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "state": "PUBLISHED", "assignerShortName": "GitHub_M", "dateReserved": "2026-03-30T19:17:10.225Z", "datePublished": "2026-04-06T15:36:52.942Z", "dateUpdated": "2026-04-06T15:36:52.942Z"}, "containers": {"cna": {"title": "vLLM affected by Server-Side Request Forgery (SSRF) in `download_bytes_from_url `", "problemTypes": [{"descriptions": [{"cweId": "CWE-918", "lang": "en", "description": "CWE-918: Server-Side Request Forgery (SSRF)", "type": "CWE"}]}], "metrics": [{"cvssV3_1": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 5.4, "baseSeverity": "MEDIUM", "confidentialityImpact": "LOW", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L", "version": "3.1"}}], "references": [{"name": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr", "tags": ["x_refsource_CONFIRM"], "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr"}], "affected": [{"vendor": "vllm-project", "product": "vllm", "versions": [{"version": ">= 0.16.0, < 0.19.0", "status": "affected"}]}], "providerMetadata": {"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "shortName": "GitHub_M", "dateUpdated": "2026-04-06T15:36:52.942Z"}, "descriptions": [{"lang": "en", "value": "vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions.\nThis can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0."}], "source": {"advisory": "GHSA-pf3h-qjgv-vcpr", "discovery": "UNKNOWN"}}}}

{}

{"cveTags": [], "descriptions": [{"lang": "en", "value": "vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions.\nThis can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0."}], "id": "CVE-2026-34753", "lastModified": "2026-04-06T16:16:36.307", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 5.4, "baseSeverity": "MEDIUM", "confidentialityImpact": "LOW", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L", "version": "3.1"}, "exploitabilityScore": 2.8, "impactScore": 2.5, "source": "security-advisories@github.com", "type": "Secondary"}]}, "published": "2026-04-06T16:16:36.307", "references": [{"source": "security-advisories@github.com", "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr"}], "sourceIdentifier": "security-advisories@github.com", "vulnStatus": "Received", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-918"}], "source": "security-advisories@github.com", "type": "Primary"}]}

{"bugzilla": {"description": "vllm: vLLM: Server-Side Request Forgery allows access to internal services via controlled batch input", "id": "2455394", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455394"}, "csaw": false, "cvss3": {"cvss3_base_score": "5.4", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L", "status": "draft"}, "cwe": "CWE-918", "details": ["A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system."], "mitigation": {"lang": "en:us", "value": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability."}, "name": "CVE-2026-34753", "package_state": [{"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis-preview/vllm-cuda-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-cpu-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-cuda-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-neuron-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-rocm-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-spyre-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Fix deferred", "package_name": "rhaiis/vllm-tpu-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-aws-cuda-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-azure-cuda-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-azure-rocm-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-cuda-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-gcp-cuda-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Fix deferred", "package_name": "rhelai3/bootc-rocm-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-agent-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-controller-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-router-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-kserve-storage-initializer-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-vllm-cpu-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-vllm-cuda-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-vllm-gaudi-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Fix deferred", "package_name": "rhoai/odh-vllm-rocm-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-04-06T15:36:52Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-34753\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-34753\nhttps://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr"], "threat_severity": "Moderate"}

{"affected": [{"configurations": [{"platform": null, "status": "affected", "versions": {"scheme": "semver", "value": "[0.16.0,0.19.0)"}}], "enrichment": {"confidence": 100.0, "confidence_source": "matching", "scores": [{"score": 100.0, "source": "matching"}]}, "original": {"product": "vllm", "source": "cna", "vendor": "vllm-project"}, "product": "vllm", "vendor": "vllm-project"}], "analysis": {"en": {"generated_at": "2026-04-06T19:40:11.285190+00:00", "value": {"mitigation_remediation": ["Upgrade vLLM to version 0.19.0 or later."], "summary": {"action": "Patch Immediately", "impact": "Server-side request forgery"}, "threat_synthesis": {"affected_systems": "The vulnerability affects versions of the vLLM project from 0.16.0 up to, but not including, 0.19.0. Systems running those releases with publicly exposed inference endpoints are susceptible. Updating to vLLM 0.19.0 or later removes the flaw.", "description_and_impact": "vLLM, an inference engine for large language models, contains a server\u2011side request forgery flaw in the download_bytes_from_url function. An attacker who can supply or modify the JSON payload sent to the batch endpoint can cause the vLLM server to initiate arbitrary HTTP or HTTPS requests. Because the function lacks URL validation or domain whitelisting, the attacker can reach any internal or external web resource visible to the host.", "risk_and_exploitability": "With a CVSS score of 5.4 the issue is categorized as moderate severity. The exploit does not require authentication and can be triggered through normal API usage for batch inference. The lack of an EPSS score and absence from the CISA KEV list suggest limited current exploitation data, but the ability to reach internal services such as cloud metadata endpoints could enable data exfiltration or lateral movement if the host is connected to sensitive networks."}}}}, "created": "2026-04-06T20:13:56.040961+00:00", "updated": "2026-04-06T21:31:54.311761+00:00", "vendors": ["vllm-project", "vllm-project$PRODUCT$vllm"]}