pydantic-settings provides settings management using Pydantic. From 2.12.0 until 2.14.2, NestedSecretsSettingsSource reads secret values from files in a configured secrets_dir. When secrets_nested_subdir=True, a directory entry inside secrets_dir that is a symbolic link pointing outside secrets_dir is followed, so files outside the configured directory are read into settings values. The same code path bypasses the documented secrets_dir_max_size protection. An attacker or lower-privileged component able to influence entries in the configured secrets directory (for example, a writable or shared secrets mount) can turn this into an unintended local file read into settings and can defeat the advertised loading-size cap. This vulnerability is fixed in 2.14.2.
pydantic
Vendor: pydantic
Security Vulnerability Index
Page 1 / 1n8n-workflows Main Commit ee25413 allows attackers to execute a directory traversal via the download_workflow function within api_server.py
Regular expression denial of service in Pydanic < 2.4.0, < 1.10.13 allows remote attackers to cause denial of service via a crafted email string.
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.