CVE-2021-29510: 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): dateofbirth: date
@validator('dateofbirth', pre=True) def skipinfinitevalues(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.
Other sources
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.
— Ubuntu
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2021-29510?
CVE-2021-29510 is classified as a high severity vulnerability because it can lead to denial of service due to excessive CPU consumption.
How do I fix CVE-2021-29510?
To mitigate CVE-2021-29510, update Pydantic to version 1.8.2, 1.7.4, or 1.6.2.
What software is affected by CVE-2021-29510?
CVE-2021-29510 affects the Pydantic library across multiple versions prior to 1.8.2.
What is the risk of not addressing CVE-2021-29510?
Not addressing CVE-2021-29510 can result in an application consuming 100% CPU, leading to performance degradation and potential service outages.
Where can I find more information about CVE-2021-29510?
Further details on CVE-2021-29510 can be found in security advisories and vulnerability databases.