Summary Two regexes in backend/openwebui/utils/middleware.py that parse <$skillId|label> skill-mention tags backtrack in O(n²) on input that contains <$ followed by a long run with no closing >. Both run synchronously, on the asyncio event loop, on every chat completion with no feature gate. Because the default deployment is a single uvicorn worker, one such input pins a CPU core inside re and freezes the entire instance for all users until the worker is killed. Any authenticated user can trigger it with one chat message; it also fires accidentally on benign retrieved content (a RAG chunk or tool output) containing the pattern.

Affected versions >= 0.9.2, < 0.10.0. Fixed in v0.10.0 (there is no 0.9.7 release). - SKILLMENTIONRE (the extract pattern) has been O(n²) since v0.9.2; exploitable on 0.9.2–0.9.5 with a large input (hundreds of KB). - v0.9.6 added a second, far more aggressive O(n²) in the strip pattern (introduced by the "keep label as readable text" change), so on 0.9.6 a small input is enough to hang the instance.

Both are fixed by the same patch.

Affected component backend/openwebui/utils/middleware.py (line numbers as of v0.9.6):

python line 2223 — used by extractskillidsfrommessages(), called unconditionally (~line 2625) SKILLMENTIONRE = re.compile(r'<\$([^|>]+)\|?[^>]>')

line 2247 — used by stripskillmentions(), called unconditionally (line 2662) stripre = re.compile(r'<\$[^|>]+\|?([^>])>')

extractskillidsfrommessages() runs before the if allskillids: block (that guard gates only skill injection, not the regex), and stripskillmentions() runs with no guard at all. Neither requires a skill to exist or any setting to be enabled. Both functions are plain synchronous calls inside the async processchatpayload coroutine, so they block the event loop; with the default UVICORNWORKERS=1 (backend/start.sh) the whole instance stalls.

Root cause [^|>] is a subset of [^>], so the quantifier pair [^|>]+ \|? [^>] is ambiguous: on input that never closes with >, [^|>]+ greedily consumes the tail, > fails, and the engine backtracks through every split point between [^|>]+ and [^>] — O(n) positions each doing O(n) work. Polynomial, not exponential, but more than enough to hang a single worker on a ~100 KB input.

Proof of concept Standalone (no Open WebUI required):

python import re, time EXTRACT = re.compile(r'<\$([^|>]+)\|?[^>]>') STRIP = re.compile(r'<\$[^|>]+\|?([^>])>') for n in (8000, 16000, 32000, 64000): s = '<$' + ('a' n) for name, rx in (('extract', EXTRACT), ('strip', STRIP)): t = time.perfcounter(); rx.search(s) print(f'n={n:>6} {name:>7} = {(time.perfcounter()-t)1000:8.1f} ms')

Time quadruples per doubling of n (textbook O(n²)); the strip pattern runs for ~6 seconds on a 64k blob and for minutes on a ~96 KB one.

End-to-end against a live instance (default config): 1. docker run ghcr.io/open-webui/open-webui:v0.9.6 on defaults. 2. Log in as any user (no admin or skill setup). 3. Send a chat message containing <$ followed by 50k+ characters with no >. 4. One CPU core pegs in re; UI and API stop responding for every user until the worker is killed.

Patch Rewrite the optional |label as a non-capturing optional group so the two quantifiers no longer overlap. Both patterns become linear; captures and substituted output are unchanged on well-formed <$id|label>, <$id|>, and bare <$id> mentions.

python SKILLMENTIONRE = re.compile(r'<\$([^|>]+)(?:\|[^>])?>') stripre = re.compile(r'<\$[^|>]+(?:\|([^>]))?>')

After the patch the same hostile input returns in under 1 ms. Shipped in v0.10.0.

Credit Reported by @Vlad-WKG, including a correct root-cause analysis and patch.