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Pydantic AI: Event loop blocked by quadratic title extraction in `web_fetch`

Moderate severity GitHub Reviewed Published Sep 17, 2026 in pydantic/pydantic-ai • Updated Oct 8, 2026

Package

pip pydantic-ai (pip)

Affected versions

>= 1.77.0, < 1.107.6
>= 2.0.0b1, < 2.44.0

Patched versions

1.107.6
2.44.0
pip pydantic-ai-slim (pip)
>= 1.77.0, < 1.107.6
>= 2.0.0b1, < 2.44.0
1.107.6
2.44.0

Description

Summary

The local web-fetch tool (web_fetch_tool, also used as the WebFetch capability's local fallback) processed responses with several steps whose running time grows quadratically with the size of certain server-controlled inputs, and ran them on the event loop: decoding the body with whichever charset the server declared, extracting the page title with a backtracking regular expression, and converting the HTML to markdown. An application that exposes this tool to untrusted prompts can be steered to fetch an attacker-controlled page of a megabyte or two that blocks the event loop for minutes, stalling every other coroutine in the process — other agent runs, other requests being served — for the duration.

This is an availability issue only. SSRF protections and the download size limit introduced in GHSA-v2xh-2vp8-57h8 are unaffected; that limit bounds how much is downloaded, not how long the response takes to process.

Details

Title extraction used a backtracking pattern over the raw response body, so a body made of repeated unterminated tag openings cost time proportional to the square of its size. The HTML-to-markdown conversion had the same shape in three of its steps: normalizing whitespace, stripping preformatted blocks, and numbering ordered lists all took time proportional to the square of a run of spaces or a list's length. All of it ran on the event loop, and the regex steps hold the interpreter lock even when moved off it, so the whole process paid for the size of a server-controlled response.

The response body was also decoded on the event loop with the codec named by the charset parameter of the response's Content-Type, looked up in Python's codec registry. That registry includes punycode, whose decoder takes time proportional to the square of its input: a response of about one megabyte labelled charset=punycode blocked the event loop for roughly half a minute, with no HTML required. The registry also includes codecs that aren't text encodings at all, such as rot_13 and base64_codec; a response labelled with one of those raised an unexpected exception out of the tool, aborting the agent run that fetched it.

Separately, the HTML-to-markdown conversion recursed once per nested element, so a page nested a few hundred elements deep raised a RecursionError out of the tool, aborting the agent run that fetched it. A JSON response nested deeper than the interpreter allows did the same. These only affect that one run.

Who Is Affected

You are affected if your application registers the local web-fetch tool (or relies on the WebFetch capability's local fallback) and exposes the agent to untrusted prompts. allowed_domains narrows the exposure to pages on those domains but does not remove it. Applications that only fetch developer-controlled URLs are not exposed to the model-chosen attack path.

Remediation

Upgrade to a patched version. The title is now found with a single linear scan, the conversion steps above run in linear time, and decoding, title extraction and conversion all run in a worker thread. A charset naming a codec that isn't a text encoding, and a page too deeply nested to convert, are reported back to the model as a failed fetch instead of aborting the run; a JSON body too deeply nested to parse is returned as plain text.

Credits

Reported privately by @BrianWillows, whose report covered the quadratic title extraction. The response decoding, the codecs that are not text encodings, and the quadratic steps in the HTML-to-markdown conversion were found while fixing it.

References

@DouweM DouweM published to pydantic/pydantic-ai Sep 17, 2026
Published to the GitHub Advisory Database Oct 8, 2026
Reviewed Oct 8, 2026
Last updated Oct 8, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(37th percentile)

Weaknesses

Inefficient Algorithmic Complexity

An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

CVE ID

CVE-2026-107290

GHSA ID

GHSA-fpf4-vwcp-v4hp

Source code

Credits

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