{"data":{"id":"6b1c8e46-5333-4a90-a8a6-780bbb96e919","title":"CVE-2024-37054: Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling ","summary":"CVE-2024-37054 is a vulnerability in MLflow (a machine learning platform) version 0.9.0 and newer that allows deserialization of untrusted data (unsafe processing of data from untrusted sources). An attacker can upload a malicious PyFunc model (a machine learning model format) that runs arbitrary code (any commands an attacker wants) on a user's computer when the model is used.","solution":"N/A -- no mitigation discussed in source.","labels":["security"],"sourceUrl":"https://nvd.nist.gov/vuln/detail/CVE-2024-37054","publishedAt":"2024-06-04T16:15:11.190Z","cveId":"CVE-2024-37054","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":0.00192,"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}}