CVE-2026-1260

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(RHOAI)", "vendor": "Red Hat"}], "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"}], "configurations": [{"nodes": [{"cpeMatch": [{"criteria": "cpe:2.3:a:google:sentencepiece:*:*:*:*:*:*:*:*", "matchCriteriaId": "E4AF7FCB-EDF3-4A3B-89FE-DBC8D23BD85F", "versionEndExcluding": "0.2.1", "vulnerable": true}], "negate": false, "operator": "OR"}]}], "cveTags": [], "descriptions": [{"lang": "en", "value": "Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure."}, {"lang": "es", "value": "Acceso a memoria no v\u00e1lido en versiones de Sentencepiece inferiores a la 0.2.1 al usar un archivo de modelo vulnerable, que no se crea en el procedimiento de entrenamiento normal."}], "id": "CVE-2026-1260", "lastModified": "2026-07-15T02:18:13.770", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "HIGH", "baseScore": 7.8, "baseSeverity": 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{"bugzilla": {"description": "sentencepiece: Sentencepiece: Invalid memory access leading to potential arbitrary code execution via a crafted model file.", "id": "2432079", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2432079"}, "csaw": false, "cvss3": {"cvss3_base_score": "7.8", "cvss3_scoring_vector": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H", "status": "draft"}, "cwe": "CWE-119", "details": ["A flaw was found in Sentencepiece. This flaw involves invalid memory access when the software processes a specially crafted, vulnerable model file. A local attacker could exploit this by tricking a user into loading such a file. Successful exploitation could lead to a denial of service, information disclosure, or potentially arbitrary code execution."], "name": "CVE-2026-1260", "package_state": [{"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Not affected", "package_name": "rhaiis-preview/vllm-cuda-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Affected", "package_name": "rhaiis/vllm-cuda-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Affected", "package_name": "rhaiis/vllm-rocm-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Not affected", "package_name": "rhaiis/vllm-spyre-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:ai_inference_server:3", "fix_state": "Not affected", "package_name": "rhaiis/vllm-tpu-rhel9", "product_name": "Red Hat AI Inference Server"}, {"cpe": "cpe:/a:redhat:enterprise_linux_ai:3", "fix_state": "Not affected", "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": "Not affected", "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": "Not affected", "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": "Not affected", "package_name": "rhelai3/bootc-gcp-cuda-rhel9", "product_name": "Red Hat Enterprise Linux AI (RHEL AI) 3"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-kserve-agent-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-kserve-controller-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-kserve-router-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-kserve-storage-initializer-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-llama-stack-core-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-openvino-model-server-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-training-cuda121-torch24-py311-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-training-cuda124-torch25-py311-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-training-cuda128-torch28-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-training-rocm62-torch24-py311-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-training-rocm62-torch25-py311-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-training-rocm64-torch28-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-vllm-cpu-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-vllm-cuda-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-vllm-gaudi-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Not affected", "package_name": "rhoai/odh-vllm-rocm-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-01-22T17:06:17Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-1260\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-1260\nhttps://github.com/google/sentencepiece/releases/tag/v0.2.1"], "threat_severity": "Important"}

{"analysis": {"en": {"generated_at": "2026-04-18T03:39:00.038124+00:00", "value": {"mitigation_remediation": ["Upgrade Sentencepiece to version 0.2.1 or later", "Validate all model files to ensure they are generated via the standard training process and originate from trusted sources", "If an upgrade is not immediately feasible, isolate the application from untrusted inputs and monitor for crashes or abnormal memory consumption"], "summary": {"action": "Apply Patch", "impact": "Memory Corruption \u2013 Potential Denial of Service"}, "threat_synthesis": {"affected_systems": "Google Sentencepiece software, all versions below 0.2.1. The vulnerability affects any deployment that loads third\u2011party model files, such as in machine\u2011learning pipelines that rely on Sentencepiece for tokenization.", "description_and_impact": "The flaw exists in Sentencepiece releases earlier than 0.2.1 when a model file that is not produced through the normal training workflow is loaded. The improper bounds check during model file parsing triggers an invalid memory access, which can cause the application to crash or corrupt execution flow. An attacker who can supply such a malicious model file may be able to disrupt service availability and, depending on the environment, create an opportunity for more serious compromise.", "risk_and_exploitability": "The CVSS score of 8.5 classifies this as high severity. An EPSS score of less than 1% indicates that the likelihood of exploitation is currently very low, and the vulnerability is not listed in the CISA KEV catalog. Exploitation requires the attacker to supply a malformed model file; therefore, the attack vector is inferred to be local or remote where the application accepts external model files. Systems that validate model files or run Sentencepiece in a sandboxed environment will reduce exposure, although the lack of public exploits means that presently the risk is largely theoretical and limited to environments that use vulnerable versions."}}}}, "created": "2026-01-23T10:27:09.142969+00:00", "updated": "2026-04-18T03:45:21.074097+00:00", "vendors": ["google", "google$PRODUCT$sentencepiece"]}