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{"bugzilla": {"description": "lupa: Lupa: Arbitrary Code Execution due to inconsistent attribute filtering", "id": "2455413", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455413"}, "csaw": false, "cvss3": {"cvss3_base_score": "8.1", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H", "status": "draft"}, "cwe": "CWE-914", "details": ["A flaw was found in Lupa, a tool that integrates Lua or LuaJIT2 runtimes into CPython. An attacker can exploit this vulnerability by bypassing attribute filtering mechanisms when accessing attributes through built-in functions like `getattr` and `setattr`. This inconsistency in applying security restrictions can allow an attacker to execute arbitrary code, potentially compromising the 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-2026-34444", "package_state": [{"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-minimal-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-minimal-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-minimal-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:satellite:6", "fix_state": "Affected", "package_name": "satellite/foreman-mcp-server-rhel9", "product_name": "Red Hat Satellite 6"}], "public_date": "2026-04-06T15:30:30Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-34444\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-34444\nhttps://github.com/scoder/lupa/security/advisories/GHSA-69v7-xpr6-6gjm"], "threat_severity": "Important"}

{"affected": [{"configurations": [{"platform": null, "status": "affected", "versions": {"scheme": "generic", "value": "[0,2.6]"}}], "enrichment": {"confidence": 100.0, "confidence_source": "cna", "scores": [{"score": 100.0, "source": "cna"}]}, "original": {"product": "lupa", "source": "cna", "vendor": "scoder"}, "product": "lupa", "vendor": "scoder"}], "analysis": {"en": {"generated_at": "2026-04-07T01:44:06.480207+00:00", "value": {"mitigation_remediation": ["Upgrade Lupa to a version newer than 2.6 to ensure attribute_filter enforcement is correct.", "If upgrading is not immediately possible, avoid executing untrusted Lua code that relies on Python attribute access functions such as getattr or setattr, or replace these calls with safer wrappers that explicitly check attribute_filter."], "summary": {"action": "Immediate Patch", "impact": "Arbitrary code execution"}, "threat_synthesis": {"affected_systems": "The vulnerability affects the scoder:Lupa product. Versions 2.6 and earlier are impacted; higher versions have fixed the attribute_filter enforcement issue.", "description_and_impact": "A flaw in the Lupa library, which embeds Lua or LuaJIT2 runtimes in CPython, causes the attribute_filter protection to be inconsistently applied when attributes are accessed through built\u2011in functions such as getattr and setattr. The result is that an attacker who can supply code that uses these operations can bypass the intended restrictions and ultimately execute arbitrary code on the host system.", "risk_and_exploitability": "The CVSS score is 7.9, indicating a high severity. EPSS data is not available and the vulnerability is not in the CISA KEV catalog, suggesting that widespread exploitation has not yet been observed. Based on the description, it is inferred that the attack vector is remote code execution via Python scripts that utilize Lupa. An attacker would need to supply a Lua script that calls Python built\u2011ins, a scenario that is feasible in many environments that allow user\u2011supplied Lua or Python code. The impact is system\u2011wide, as arbitrary code can be run with the privileges of the process that loaded Lupa."}}}}, "created": "2026-04-06T20:14:03.788311+00:00", "updated": "2026-04-07T06:54:59.469851+00:00", "vendors": ["scoder", "scoder$PRODUCT$lupa"]}