Focus on pydantic vulnerabilities and metrics.
Last updated: 08 Mar 2026, 23:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with pydantic. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total pydantic CVEs: 4
Earliest CVE date: 13 May 2021, 19:15 UTC
Latest CVE date: 06 Feb 2026, 21:16 UTC
Latest CVE reference: CVE-2026-25580
30-day Count (Rolling): 0
365-day Count (Rolling): 2
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): -100.0%
Year Variation (Calendar): 100.0%
Month Growth Rate (30-day Rolling): -100.0%
Year Growth Rate (365-day Rolling): 100.0%
Average CVSS: 1.25
Max CVSS: 5.0
Critical CVEs (≥9): 0
| Range | Count |
|---|---|
| 0.0-3.9 | 3 |
| 4.0-6.9 | 1 |
| 7.0-8.9 | 0 |
| 9.0-10.0 | 0 |
These are the five CVEs with the highest CVSS scores for pydantic, sorted by severity first and recency.
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.
Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.34.0 to before 1.51.0, a path traversal vulnerability in the Pydantic AI web UI allows an attacker to serve arbitrary JavaScript in the context of the application by crafting a malicious URL. In affected versions, the CDN URL is constructed using a version query parameter from the request URL. This parameter is not validated, allowing path traversal sequences that cause the server to fetch and serve attacker-controlled HTML/JavaScript from an arbitrary source on the same CDN, instead of the legitimate chat UI package. If a victim clicks the link or visits it via an iframe, attacker-controlled code executes in their browser, enabling theft of chat history and other client-side data. This vulnerability only affects applications that use Agent.to_web to serve a chat interface and clai web to serve a chat interface from the CLI. These are typically run locally (on localhost), but may also be deployed on a remote server. This vulnerability is fixed in 1.51.0.
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.