vulnerability / NVI-00018649 / Revision 1
Use of "infinity" as an input to datetime and date fields causes infinite loop in pydantic
GitHub lists this reviewed advisory for pydantic, pydantic, pydantic. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.
This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.
01 / Meaning
Why it matters
Published affected context helps an owner decide what evidence to gather. This report has not examined your application.
3.3 medium
GitHub source severity; exploitability and local consequence require separate evidence.
As of · GitHub Advisory Database
02 / Applicability
The affected context
Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.
pydantic
source listed< 1.6.2
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
pydantic
source listed>= 1.8, < 1.8.2
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
pydantic
source listed>= 1.7, < 1.7.4
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
03 / Response
What to do next
- investigate
Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.
Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system. GitHub Advisory Database
A fixed release is not deployment evidence.
Check current vendor support and compatibility. After making a change, verify the deployed build and relevant runtime conditions. Keep the evidence, owner and time. An updated lockfile alone does not prove what is running.
04 / Limits
What this report cannot establish
- Customer inventory, deployment, reachability and exploitation have not been inspected.
05 / Provenance
Sources you can inspect
Each assertion retains its source. Mirrored records may repeat one underlying publisher; additional links are not independent corroboration.
- GHSA-5jqp-qgf6-3pvh ↗
GitHub Advisory Database · vendor advisory
Observed . GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.
Public revision history
Only published revisions appear here. Routine fetches do not create a material-change event.
- Revision 1 ·
First indexed by Null; source disclosure date is shown separately.