Vulnerabilities

Pydantic AI is a Python agent framework for building Generative AI applications. In versions 1.65.0 through 1.105.0, and 2.0.0b1 through 2.0.0b5, a client that submits message history to a Pydantic AI UI adapter (such as the Vercel AI adapter) can reference arbitrary files in the application's model-provider or cloud-storage account. While file URL parts are validated against a scheme allowlist, UploadedFile references — which point to a file by provider file ID or cloud-storage URI (e.g. s3://…, gs://…) — were forwarded without validation. Because the provider resolves an UploadedFile using the server-side identity (IAM role, service account, or provider API key) rather than the client's, an attacker can craft message history to make the server read objects from its own account or other tenants, given a referenceable identifier. Exploitation requires a valid file identifier, which is not always unguessable depending on how the application names objects. This issue has been fixed in versions 1.106.0 and 2.0.0b6.

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 AI is a Python agent framework for building applications and workflows with Generative AI. In versions 1.56.0 through 1.101.0, 2.0.0b1, and 2.0.0b2, the cloud-metadata blocklist could be bypassed by encoding the metadata IP in an IPv6 transition form that the previous fix, CVE-2026-46678, did not decode, exposing cloud IAM short-term credentials. The previous remediation decoded only IPv4-mapped IPv6, 6to4, and the NAT64 well-known prefix, so the metadata guarantee did not hold for the remaining transition forms: IPv4-compatible IPv6 (::a.b.c.d), the NAT64 RFC 8215 local-use prefix (64:ff9b:1::/48), operator-chosen NAT64 prefixes, and ISATAP. The IPv6 wrapper is then delivered to the underlying IPv4 metadata endpoint. This occurs when an application using Pydantic AI opts a URL into force_download='allow-local' (which disables the default block on private/internal IPs) and runs on a network that actually routes the affected IPv6 transition forms: NAT64-configured networks (IPv6-only or dual-stack-with-NAT64 deployments, including some Kubernetes setups) for the NAT64 variants, or networks with an ISATAP tunnel for ISATAP. A standard dual-stack cloud VM or container does not route these forms and is not affected in practice. The IPv4-compatible and Teredo variants are deprecated and addressed as defense-in-depth. This is an incomplete fix of GHSA-cqp8-fcvh-x7r3 / CVE-2026-46678 (itself a follow-up to CVE-2026-25580). This issue has been fixed in version 2.0.0b3.

Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.0.26 to before 1.56.0, aServer-Side Request Forgery (SSRF) vulnerability exists in Pydantic AI's URL download functionality. When applications accept message history from untrusted sources, attackers can include malicious URLs that cause the server to make HTTP requests to internal network resources, potentially accessing internal services or cloud credentials. This vulnerability only affects applications that accept message history from external users. This vulnerability is fixed in 1.56.0.

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.