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work around this risk using a validator to catch these values, brief demo:\n\nfrom datetime import date\nfrom pydantic import BaseModel, validator\n\nclass DemoModel(BaseModel):\n    date_of_birth: date\n\n    @validator('date_of_birth', pre=True)\n    def skip_infinite_values(cls, v):\n        try:\n            seconds = float(v)\n        except (ValueError, TypeError):\n            return v\n        else:\n            if seconds == float('inf'):\n                return date.max\n            elif seconds == float('-inf'):\n                return date.min\n            else:\n                return seconds\n\nNote: this is not an ideal solution (in particular you'll need a slightly different function for datetimes), instead of a hack like this you should upgrade pydantic.\n\nIf you are not using v1.8.x, v1.7.x or v1.6.x and are unable to upgrade to a fixed version of pydantic, please create an issue requesting a back-port, and we will endeavour to release a patch for earlier versions of 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