Impact
Passing either 'infinity', 'inf' or float('inf') (or their negatives) to datetime or date fields causes validation to run forever with 100% CPU usage (on one CPU).
Patches
Pydantic is be patched with fixes available in the following versions:
v1.8.2
v1.7.4
v1.6.2
All these versions are available on pypi, and will be available on conda-forge soon.
See the changelog for details.
Workarounds
If you absolutely can't upgrade, you can work around this risk using a validator to catch these values, brief demo:
from datetime import date
from pydantic import BaseModel, validator
class DemoModel(BaseModel):
date_of_birth: date
@validator('date_of_birth', pre=True)
def skip_infinite_values(cls, v):
try:
seconds = float(v)
except (ValueError, TypeError):
return v
else:
if seconds == float('inf'):
return date.max
elif seconds == float('-inf'):
return date.min
else:
return seconds
Note: 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.
If 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 pydantic.
References
This was fixed in commit 7e83fdd.
References
Impact
Passing either 'infinity', 'inf' or float('inf') (or their negatives) to datetime or date fields causes validation to run forever with 100% CPU usage (on one CPU).
Patches
Pydantic is be patched with fixes available in the following versions:
All these versions are available on pypi, and will be available on conda-forge soon.
See the changelog for details.
Workarounds
If you absolutely can't upgrade, you can work around this risk using a validator to catch these values, brief demo:
from datetime import date
from pydantic import BaseModel, validator
class DemoModel(BaseModel):
date_of_birth: date
Note: 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.
If 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 pydantic.
References
This was fixed in commit 7e83fdd.
References