GHSA-ffq3-xpv3-j92q

## Summary **Type:** Algorithmic-complexity DoS in reference-link definition handling. A markdown document with N reference-link definitions of the same key (or many distinct keys) takes O(N²) parser time. 5000 repeated `[a]: u\n` definitions take ~1.1 second; 10000 → ~4.5 seconds. **File:** `src/mistune/block_parser.py` (reference-link def parsing) and the surrounding `ref_links` env-dictionary handling. **Root cause:** every reference definition is parsed by scanning forward from each candidate position. The `unikey` normalisation runs per-def, the dictionary insert is per-def, and the lookup-by-label-then-iterate-defs path is linear in the number of stored defs. For input with N defs, the total work is O(N²). ## Affected Code `src/mistune/block_parser.py` — reference-definition rule fires on every line that matches `[label]: url`. For each one: - `unikey(label)` is called (linear scan of the label). - The def is appended to `state.env['ref_links']`. - Later inline-link resolution looks up by `unikey(label)` in the dict (O(1)) but the surrounding parser revisits the def list for paragraph-vs-def disambiguation. The cumulative parse time grows as the square of the number of defs. **Why it's wrong:** the parser does not amortise the def-list scan. A single forward pass with a hash-keyed dict (already in place) plus a per-line classifier should make this O(N). ## Exploit Chain 1. Application uses mistune to render attacker-supplied markdown. No plugins required. 2. Attacker submits a 35 KB document of `[a]: u\n` repeated 5000 times followed by `[click][a]`. 3. CPU pegs for ~1.1 seconds. 10000 defs → ~4.5 s. 20000 → ~18 s. Doubling input quadruples time. ## Security Impact **Attacker capability:** small input → large CPU. Predictable scaling. Can be repeated. **Preconditions:** application uses `mistune.create_markdown()` (default config) on attacker-supplied markdown. Worth noting: the `ref_links` dictionary persists for the lifetime of the parse, so a long document with many defs builds up memory; with N defs of attacker-chosen length, the per-def normalisation cost compounds. **Differential:** PoC-verified against [email protected], default config: ```python import mistune, time md = mistune.create_markdown() for n in [1000, 2000, 5000, 10000]: s = '[a]: u\n' * n + '[click][a]' t = time.time() md(s) print(f' ref defs * {n} ({len(s)}b): {(time.time() - t) * 1000:.0f}ms') # Output (Python 3.13, Linux, 2.5GHz CPU): # ref defs * 1000 ( 7012b): 46ms # ref defs * 2000 (14012b): 186ms # ref defs * 5000 (35012b): 1121ms # ref defs * 10000 (70012b): 4400ms ``` The patched build (with the surrounding parser amortised to O(N)) keeps the time linear. ## Suggested Fix Replace the per-def re-scan with a single forward pass that classifies each line into `ref_def | paragraph | other` once and only inserts into `ref_links` once per def. The dict already exists; the wasted work is in the surrounding scan loop, not in the dict operations. A regression test asserting that `md('[a]: u\n' * 50_000 + '[click][a]')` completes in under 1 second would catch any regression.