{"dataType": "CVE_RECORD", "dataVersion": "5.1", "cveMetadata": {"cveId": "CVE-2025-46722", "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "state": "PUBLISHED", "assignerShortName": "GitHub_M", "dateReserved": "2025-04-28T20:56:09.084Z", "datePublished": "2025-05-29T16:36:12.879Z", "dateUpdated": "2025-05-29T18:13:02.824Z"}, "containers": {"cna": {"title": "vLLM has a Weakness in MultiModalHasher Image Hashing Implementation", "problemTypes": [{"descriptions": [{"cweId": "CWE-1288", "lang": "en", "description": "CWE-1288: Improper Validation of Consistency within Input", "type": "CWE"}]}, {"descriptions": [{"cweId": "CWE-1023", "lang": "en", "description": "CWE-1023: Incomplete Comparison with Missing Factors", "type": "CWE"}]}], "metrics": [{"cvssV3_1": {"attackComplexity": "HIGH", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 4.2, "baseSeverity": "MEDIUM", "confidentialityImpact": "LOW", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:H/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-c65p-x677-fgj6", "tags": ["x_refsource_CONFIRM"], "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6"}, {"name": "https://github.com/vllm-project/vllm/pull/17378", "tags": ["x_refsource_MISC"], "url": "https://github.com/vllm-project/vllm/pull/17378"}, {"name": "https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848", "tags": ["x_refsource_MISC"], "url": "https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848"}], "affected": [{"vendor": "vllm-project", "product": "vllm", "versions": [{"version": ">= 0.7.0, < 0.9.0", "status": "affected"}]}], "providerMetadata": {"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "shortName": "GitHub_M", "dateUpdated": "2025-05-29T16:36:12.879Z"}, "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\u2019s 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."}], "source": {"advisory": "GHSA-c65p-x677-fgj6", "discovery": "UNKNOWN"}}, "adp": [{"metrics": [{"other": {"type": "ssvc", "content": {"timestamp": "2025-05-29T18:12:29.713264Z", "id": "CVE-2025-46722", "options": [{"Exploitation": "none"}, {"Automatable": "no"}, {"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": "2025-05-29T18:13:02.824Z"}}]}}

{"dataType": "CVE_RECORD", "containers": {"adp": [{"title": "CISA ADP Vulnrichment", "metrics": [{"other": {"type": "ssvc", "content": {"id": "CVE-2025-46722", "role": "CISA Coordinator", "options": [{"Exploitation": "none"}, {"Automatable": "no"}, {"Technical Impact": "partial"}], "version": "2.0.3", "timestamp": "2025-05-29T18:12:29.713264Z"}}}], "providerMetadata": {"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "shortName": "CISA-ADP", "dateUpdated": "2025-05-29T18:12:51.565Z"}}], "cna": {"title": "vLLM has a Weakness in MultiModalHasher Image Hashing Implementation", "source": {"advisory": "GHSA-c65p-x677-fgj6", "discovery": "UNKNOWN"}, "metrics": [{"cvssV3_1": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 4.2, "attackVector": "NETWORK", "baseSeverity": "MEDIUM", "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L", "integrityImpact": "NONE", "userInteraction": "NONE", "attackComplexity": "HIGH", "availabilityImpact": "LOW", "privilegesRequired": "LOW", "confidentialityImpact": "LOW"}}], "affected": [{"vendor": "vllm-project", "product": "vllm", "versions": [{"status": "affected", "version": ">= 0.7.0, < 0.9.0"}]}], "references": [{"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6", "name": "https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6", "tags": ["x_refsource_CONFIRM"]}, {"url": "https://github.com/vllm-project/vllm/pull/17378", "name": "https://github.com/vllm-project/vllm/pull/17378", "tags": ["x_refsource_MISC"]}, {"url": "https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848", "name": "https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848", "tags": ["x_refsource_MISC"]}], "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\u2019s 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."}], "problemTypes": [{"descriptions": [{"lang": "en", "type": "CWE", "cweId": "CWE-1288", "description": "CWE-1288: Improper Validation of Consistency within Input"}]}, {"descriptions": [{"lang": "en", "type": "CWE", "cweId": "CWE-1023", "description": "CWE-1023: Incomplete Comparison with Missing Factors"}]}], "providerMetadata": {"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "shortName": "GitHub_M", "dateUpdated": "2025-05-29T16:36:12.879Z"}}}, "cveMetadata": {"cveId": "CVE-2025-46722", "state": "PUBLISHED", "dateUpdated": "2025-05-29T18:13:02.824Z", "dateReserved": "2025-04-28T20:56:09.084Z", "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "datePublished": "2025-05-29T16:36:12.879Z", "assignerShortName": "GitHub_M"}, "dataVersion": "5.1"}

{"configurations": [{"nodes": [{"cpeMatch": [{"criteria": "cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*", "matchCriteriaId": "08DBEEAC-7BAC-4A44-894B-5F544B5CF9D3", "versionEndExcluding": "0.9.0", "versionStartIncluding": "0.7.0", "vulnerable": true}], "negate": false, "operator": "OR"}]}], "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\u2019s 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 m\u00e9todo de hash de im\u00e1genes. Actualmente, serializa los objetos PIL.Image.Image utilizando \u00fanicamente obj.tobytes(), que devuelve \u00fanicamente los datos de p\u00edxeles sin procesar, sin incluir metadatos como la forma de la imagen (ancho, alto, modo). Como resultado, dos im\u00e1genes de diferentes tama\u00f1os (p. ej., 30x100 y 100x30) con la misma secuencia de bytes de p\u00edxeles podr\u00edan generar el mismo valor hash. Esto puede provocar colisiones de hash, aciertos de cach\u00e9 incorrectos e incluso fugas de datos o riesgos de seguridad. Este problema se ha corregido en la versi\u00f3n 0.9.0."}], "id": "CVE-2025-46722", "lastModified": "2025-06-24T18:12:30.023", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "HIGH", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 4.2, "baseSeverity": "MEDIUM", "confidentialityImpact": "LOW", "integrityImpact": "NONE", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L", "version": "3.1"}, "exploitabilityScore": 1.6, "impactScore": 2.5, "source": "security-advisories@github.com", "type": "Secondary"}, {"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 7.3, "baseSeverity": "HIGH", "confidentialityImpact": "LOW", "integrityImpact": "LOW", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L", "version": "3.1"}, "exploitabilityScore": 3.9, "impactScore": 3.4, "source": "nvd@nist.gov", "type": "Primary"}]}, "published": "2025-05-29T17:15:21.523", "references": [{"source": "security-advisories@github.com", "tags": ["Patch"], "url": "https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848"}, {"source": "security-advisories@github.com", "tags": ["Issue Tracking", "Patch"], "url": "https://github.com/vllm-project/vllm/pull/17378"}, {"source": "security-advisories@github.com", "tags": ["Vendor Advisory"], "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6"}], "sourceIdentifier": "security-advisories@github.com", "vulnStatus": "Analyzed", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-1023"}, {"lang": "en", "value": "CWE-1288"}], "source": "security-advisories@github.com", "type": "Primary"}]}

{"bugzilla": {"description": "vllm: vLLM has a Weakness in MultiModalHasher Image Hashing Implementation", "id": "2369221", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2369221"}, "csaw": false, "cvss3": {"cvss3_base_score": "4.2", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L", "status": "draft"}, "cwe": "(CWE-1023|CWE-1288)", "details": ["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\u2019s 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.", "An image based hash collision flaw was found in vLLM due to insufficient distinct hashing. This flaw allows an attacker to poison the cache in a vLLM instance, which may lead to inconsistent or unexpected output."], "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-2025-46722", "package_state": [{"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-rocm-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-amd-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-aws-nvidia-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-azure-amd-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-azure-nvidia-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-gcp-nvidia-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-intel-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/bootc-nvidia-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/instructlab-amd-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/instructlab-intel-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/instructlab-nvidia-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:1", "fix_state": "Fix deferred", "package_name": "rhelai1/ui-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI)"}], "public_date": "2025-05-29T16:36:12Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2025-46722\nhttps://nvd.nist.gov/vuln/detail/CVE-2025-46722\nhttps://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848\nhttps://github.com/vllm-project/vllm/pull/17378\nhttps://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6"], "threat_severity": "Moderate"}

{"created": "2025-06-23T19:31:58.746218+00:00", "updated": "2025-06-23T19:31:58.746296+00:00", "vendors": ["vllm-project", "vllm-project$PRODUCT$vllm"]}