{"data":{"id":"6928b43a-bb2f-4924-945d-64a3c5f00ec4","title":"CVE-2024-37057: Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabli","summary":"CVE-2024-37057 is a vulnerability in MLflow (an open-source machine learning platform) versions 2.0.0rc0 and newer that allows deserialization of untrusted data (converting data from an untrusted source back into executable code). An attacker could upload a malicious TensorFlow model (a type of machine learning model) that runs arbitrary code (any commands an attacker chooses) on a user's computer when the model is loaded or used.","solution":"N/A -- no mitigation discussed in source.","labels":["security"],"sourceUrl":"https://nvd.nist.gov/vuln/detail/CVE-2024-37057","publishedAt":"2024-06-04T16:15:11.800Z","cveId":"CVE-2024-37057","cweIds":["CWE-502","CWE-502"],"cvssScore":"8.8","cvssSeverity":"high","severity":"high","attackType":["model_poisoning"],"issueType":"vulnerability","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":["MLflow"],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":"unknown","epssScore":0.00519,"patchAvailable":null,"disclosureDate":null,"capecIds":["CAPEC-586"],"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":null,"atlasIds":null}}