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Not in CISA KEV
LOW severity

GHSA-5jqp-qgf6-3pvh pydantic

LOWFix: pydantic/pydantic@1c24f1d

GHSA-5jqp-qgf6-3pvh is a low-severity (CVSS 3.3) CWE-835 vulnerability in pydantic. A fix is available for pydantic — see the affected versions and patch details below.

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

Also known asCVE-2021-29510PYSEC-2021-47
Published
May 13, 2021
Updated
Jul 8, 2026
Affected
3 pkgs
Patched
3 / 3
Exploits
None indexed
Exploitation data as of Sep 19, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
1.0%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs60th percentile — riskier than 60% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-5jqp-qgf6-3pvh plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 376,715 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

3 pkgs affected
🐍pydantic🐍pydantic🐍pydantic

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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.

Affected Packages

3 total 3 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpydanticall versions1.6.2pip install --upgrade 'pydantic==1.6.2'
🐍PyPIpydantic1.8&&< 1.8.21.8.2pip install --upgrade 'pydantic==1.8.2'
🐍PyPIpydantic1.7&&< 1.7.41.7.4pip install --upgrade 'pydantic==1.7.4'

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for pydantic, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update pydantic to 1.6.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-5jqp-qgf6-3pvh is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-5jqp-qgf6-3pvh can be triaged on real exposure rather than presence alone.

Tailored to GHSA-5jqp-qgf6-3pvh. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Frequently Asked Questions

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, validato
O3 Security · Impact-Aware SCA

Is GHSA-5jqp-qgf6-3pvh in your dependencies?

O3 Security finds GHSA-5jqp-qgf6-3pvh across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

GHSA-5jqp-qgf6-3pvh: pydantic (Low 3.3) | O3 Security