Affected: open-webui

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform. From 0.9.6 until 0.11.0, the built-in knowledge search path in backend/open_webui/tools/knowledge_fs.py and backend/open_webui/tools/builtin.py let a chat participant choose a pattern used to grep knowledge files. Patterns containing regex metacharacters were compiled with Python's backtracking re engine and run against every line of every reachable file with no time limit, so a crafted pattern such as (x|x)*y and one matching uploaded file line can pin one CPU core and block the event loop. This causes availability impact for every other user of the affected worker. This issue is fixed in 0.11.0.

Affected: PyPI: open-webui

## Summary The built-in knowledge search tools let a chat participant choose the pattern used to grep knowledge files. Patterns containing regex metacharacters were compiled with Python's backtracking `re` engine and run against every line of every reachable file, with no time limit anywhere on that path. A single crafted pattern and a single short line of matching text pin one CPU core for as long as the attacker wants, and because the search runs synchronously inside the event loop, that worker serves nobody else while it spins.

## Preconditions - Default configuration. The knowledge builtin tool group is enabled by default, and with `ENABLE_KB_EXEC` at its default of `False` the model is handed `grep_knowledge_files`, which is the affected path. - Any authenticated user, no elevated role and no workspace permission. - One file the attacker can read. `USER_PERMISSIONS_CHAT_FILE_UPLOAD` defaults to true and a user always has read access to their own upload, so both halves of the input are attacker-supplied. - A model willing to call the tool with the attacker's literal pattern. This is the one non-deterministic step: it is reliable in practice by instructing the model in your own chat, but it is not guaranteed on a given turn. - Deployments running with `UVICORN_WORKERS` at its default of 1 lose the whole instance; multi-worker deployments lose one worker per request.

## Impact Availability, against every other user of the affected worker. Cost scales exponentially with t…