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.
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.
Update pydantic to 1.6.2; pydantic to 1.8.2; pydantic to 1.7.4 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanUse of "infinity" as an input to datetime and date fields causes infinite loop in pydantic affects pydantic (pip), pydantic (pip), pydantic (pip). Severity is medium. 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.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| pydanticpip | <1.6.2 | 1.6.2 |
| pydanticpip | >=1.8,<1.8.2 | 1.8.2 |
| pydanticpip | >=1.7,<1.7.4 | 1.7.4 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate pydantic to 1.6.2; pydantic to 1.8.2; pydantic to 1.7.4 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanUse of "infinity" as an input to datetime and date fields causes infinite loop in pydantic affects pydantic (pip), pydantic (pip), pydantic (pip). Severity is medium. 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.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| pydanticpip | <1.6.2 | 1.6.2 |
| pydanticpip | >=1.8,<1.8.2 | 1.8.2 |
| pydanticpip | >=1.7,<1.7.4 | 1.7.4 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard