Question 1

Is there a bug in pydantic?

Accepted Answer

PYSEC-2021-47: advisory Pydantic is a data validation and settings management using Python type hinting. In affected versions 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). Pydantic has been patched with fixes available in the following versions: v1.8.2, v1.7.4, v1.6.2. All these versions are available on pypi(https://pypi.org/project/pydantic/#history), and will be available on conda-forge(https://anaconda.org/conda-forge/pydantic) soon. See the changelog(https://pydantic-docs.helpmanual.io/) for details. If you absolutely can't upgrade, you can work around this risk using a validator(https://pydantic-docs.helpmanual.io/usage/validators/) to catch these values. 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 at https://github.com/samuelcolvin/pydantic/issues requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic. Fixed in pydantic 7e83fdd2563ffac081db7ecdf1affa65ef38c468.

Question 2

Is there a bug in pydantic 2.0.0?

Accepted Answer

Pydantic regular expression denial of service Regular expression denial of service in Pydantic < 2.4.0, < 1.10.13 allows remote attackers to cause denial of service via a crafted email string. Fixed in pydantic 2.4.0.

Question 3

Is there a bug in pydantic?

Accepted Answer

Use of "infinity" as an input to datetime and date fields causes infinite loop in pydantic 
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.
 Fixed in pydantic 1.6.2.

Question 4

Is there a bug in pydantic 2.5.0?

Accepted Answer

model_dump with mode='json' loses timezone on datetime fields datetime fields with timezone info were serialized as naive strings in 2.5.0. Fixed in 2.5.2. Fixed in pydantic 2.5.2.

pydantic known bugs

Known bugs

API access

medium	any	7e83fdd2563ffac081db7ecdf1affa65ef38c468	PYSEC-2021-47: advisory Pydantic is a data validation and settings management using Python type hinting. In affected versions 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). Pydantic has been patched with fixes available in the following versions: v1.8.2, v1.7.4, v1.6.2. All these versions are available on pypi(https://pypi.org/project/pydantic/#history), and will be available on conda-forge(https://anaconda.org/conda-forge/pydantic) soon. See the changelog(https://pydantic-docs.helpmanual.io/) for details. If you absolutely can't upgrade, you can work around this risk using a validator(https://pydantic-docs.helpmanual.io/usage/validators/) to catch these values. 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 at https://github.com/samuelcolvin/pydantic/issues requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic.	fixed	osv:PYSEC-2021-47
medium	2.0.0	2.4.0	Pydantic regular expression denial of service Regular expression denial of service in Pydantic < 2.4.0, < 1.10.13 allows remote attackers to cause denial of service via a crafted email string.	fixed	osv:GHSA-mr82-8j83-vxmv
medium	any	1.6.2	Use of "infinity" as an input to datetime and date fields causes infinite loop in pydantic 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.	fixed	osv:GHSA-5jqp-qgf6-3pvh
medium	2.5.0	2.5.2	model_dump with mode='json' loses timezone on datetime fields datetime fields with timezone info were serialized as naive strings in 2.5.0. Fixed in 2.5.2.	closed	github:#8185