{"dataType": "CVE_RECORD", "dataVersion": "5.2", "cveMetadata": {"cveId": "CVE-2026-33625", "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "state": "PUBLISHED", "assignerShortName": "GitHub_M", "dateReserved": "2026-03-23T14:24:11.617Z", "datePublished": "2026-09-18T17:13:37.133Z", "dateUpdated": "2026-09-18T17:13:37.133Z"}, "containers": {"cna": {"title": "LMDeploy vulnerable to arbitrary code execution via eval() of untrusted quant_dtype in model config loading", "problemTypes": [{"descriptions": [{"cweId": "CWE-400", "lang": "en", "description": "CWE-400: Uncontrolled Resource Consumption", "type": "CWE"}]}], "metrics": [{"cvssV3_1": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "HIGH", "baseScore": 8.8, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "REQUIRED", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H", "version": "3.1"}}], "references": [{"name": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-3hmm-rh5q-gwwr", "tags": ["x_refsource_CONFIRM"], "url": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-3hmm-rh5q-gwwr"}, {"name": "https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3", "tags": ["x_refsource_MISC"], "url": "https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3"}], "affected": [{"vendor": "InternLM", "product": "lmdeploy", "versions": [{"version": ">= 0.12.1, < 0.12.3", "status": "affected"}]}], "providerMetadata": {"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "shortName": "GitHub_M", "dateUpdated": "2026-09-18T17:13:37.133Z"}, "descriptions": [{"lang": "en", "value": "LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions 012.1 through 0.12.2 contain a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publishing a malicious HuggingFace model with a crafted `quantization_config.quant_dtype` value. When a user loads the model with lmdeploy, the `quant_dtype` is passed to `eval(f'torch.{quant_dtype}')` without any validation. Version 0.12.3 contains a patch."}], "source": {"advisory": "GHSA-3hmm-rh5q-gwwr", "discovery": "UNKNOWN"}}}}

{}

{"affected": [{"affectedData": [{"product": "lmdeploy", "vendor": "InternLM", "versions": [{"status": "affected", "version": ">= 0.12.1, < 0.12.3"}]}], "source": "[email protected]"}], "cveTags": [], "descriptions": [{"lang": "en", "value": "LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions 012.1 through 0.12.2 contain a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publishing a malicious HuggingFace model with a crafted `quantization_config.quant_dtype` value. When a user loads the model with lmdeploy, the `quant_dtype` is passed to `eval(f'torch.{quant_dtype}')` without any validation. Version 0.12.3 contains a patch."}], "id": "CVE-2026-33625", "lastModified": "2026-09-18T18:17:06.640", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "HIGH", "baseScore": 8.8, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "REQUIRED", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H", "version": "3.1"}, "exploitabilityScore": 2.8, "impactScore": 5.9, "source": "[email protected]", "type": "Secondary"}]}, "published": "2026-09-18T18:17:06.640", "references": [{"source": "[email protected]", "url": "https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3"}, {"source": "[email protected]", "url": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-3hmm-rh5q-gwwr"}], "sourceIdentifier": "[email protected]", "vulnStatus": "Received", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-400"}], "source": "[email protected]", "type": "Primary"}]}

{}

{}